Construction of Curriculum Ideological and Political Collaborative Education Mechanism Based on Edge Computing and Neural Network Algorithm.

The ideological and political collaborative education mechanism is an important course teaching method that uses all courses as a carrier to cultivate students' all-round development in morality, intelligence, physique, and beauty. The purpose of this paper is to conduct a better research on the construction of the ideological and political collaborative education mechanism by building models based on edge computing and neural network algorithm. This paper first gave a general introduction to edge computing and neural network algorithm and then analyzed the current situation of ideological and political courses in a certain school. Then, edge computing and neural network algorithm were introduced into the analysis of an important course teaching method that used all courses as a carrier to cultivate students' comprehensive development in morality, intelligence, physique, and beauty. The BP neural network model was established. Through analysis and comparison, the experimental results showed that 56.47% of the students believed that the impact of personal morality on the future development of college students was the first in the relationship between "virtue" and "talent." More than half of the students believed that the "virtue" of building morality and cultivating people was mainly civic morality, and about 30% of the students thought that the main value was loving the party and patriotism, which meant that most students believed that the main value of building morality and cultivating people was to establish morality.

1. Introduction

With the rapid progress of domestic education, the emphasis on students' ideological and political training has gradually increased. Universities in China have also input many study and field visits on the ideological and political collaborative training mechanism. Most colleges and universities divide the ideological and political collaborative training mechanism into two parts. The first is that teachers' collaborative education concept needs to be improved, and the second is to add and change students' subject courses. The concept of edge computing is to approach the target object or data source and use a platform to obtain the network, computing, and storage capabilities to provide customers with the best service at the closest distance. The process of the neural network algorithm is calculated through the logic rules of the target object. These two algorithms are effective in terms of user response, distributed storage, and parallel cooperative processing.

In present-day's multiproject progress in the field of education, in order to help students cultivate morality and correct their thinking, teachers should establish an all-round collaborative education mechanism when putting effect ideological education and political training activities in colleges due to the complex teaching content, so as to effectively strengthen the effect of teach and increase the effect of ideological and political education, which also has far-reaching significance for developing and strengthening the political concepts of university students. The real-time performance of edge computing and neural network algorithm is good, so their application scope is so extensive. In recent years, scholars have studied the ideological and political collaborative education mechanism, but there are few applications and researches on edge computing and neural network algorithm in this area. Therefore, this paper applies edge algorithm and neural network algorithm to the study of ideological and political collaborative education mechanism, which has both theoretical and practical significance.

The innovation of this paper lies in (1) the use of edge computing and neural network algorithm to study the ideological and political collaborative mechanism makes the content of the article more scientific and accurate, and the neural network can simulate the way of thinking of human, which will be a good research direction. (2) It applies edge computing and neural network algorithms to the research on the construction of ideological and political courses collaborative education mechanism and obtains the current situation of college ideological and political courses awareness through questionnaires. Then, it makes a BP neural network model to conduct a better research on the curriculum ideological and political collaborative education institutions.

2. Related Work

Because the country attaches more and more importance to education, more and more scholars have conducted study on the construction of the ideological and political collaborative education mechanism. In recent years, Cheng et al. discussed the teaching practice of the course of material subject from the respects of tutoring concept, teaching programming, instructional process, teaching basic mechanism, teaching content, teaching content, etc., with the background of the Internet [1]. But Cheng et al. did not point out the framework of the entire education mechanism system in the text. Qi and Xiao, on the other hand, said that to construct a practical teaching evaluation mechanism for ideological and political theory courses, it is necessary to improve the evaluation subject team and take teachers and students as contributors to build and improve the assessment institution with detailed and clear criteria, ways and details, and build a three-dimensional, multidimensional, interactive reward and punishment feedback mechanism and system [2]. However, the materials adopted in the article were old and not new enough. Getting the “ university English” course as an example, Wu researched and discussed the relationship between blended teaching and ideological and political teaching, analyzed the teaching method of blended teaching, and proposed two parts of the reform evaluation system, hoping to bring reference and help [3]. But it just blindly stated the mixed relationship between the two, without using appropriate theoretical knowledge for auxiliary proof. After referring to the research results of other scholars, Liu et al. believed that the teaching of ideological and political theory courses in colleges and universities should incorporate scientific spirit. Based on family education, guided by teach in colleges and supported by social education, it should comprehensively enhance the ideals and beliefs of college students and cultivate a profound patriotism and a strong sense of social responsibility in college students [4]. But Liu et al. did not well point out the importance of scientific spirit in the ideological and political theory courses.

In the research of edge computing, Taleb et al. introduced the research overview of MEC, facilitating different orchestration deployment options [5]. But the formula used was not reasonable. In this research, Li et al. introduced IoT deep learning into the edge computing environment. Because the edge node processing problem cannot be dealt with comprehensively, a new offloading strategy was also designed to optimize the performance of edge computing [6]. However, the scope of the experiments was not extensive enough, resulting in imprecise data. Later, Su et al. used the strong self-learning ability and data fitting ability of neural network and proposed a comprehensive error compensation method based on improved neural network algorithm. An improved algorithm that introduced a steepness factor and an amplification factor was proposed to improve the convergence efficiency due to the slow decline of the neuron error surface [7]. But the overview of the mental network algorithm above is not complete.

3. Methods of Constructing the Ideological and Political Collaborative Education Mechanism

3.1. Edge Computing

Edge computing can be said to be a brand-new computing system and technology, which can transform computing power from the cloud to the edge of the network. It can be intelligent, make business real time, and process data fast. And it does a good job in security and privacy protection [8]. After consulting the data, the edge computing reference architecture is obtained, as shown in Figure 1.

Figure 1

Edge computing reference architecture.

This section mainly introduces the current classical edge detection operators and compares their detection results to intuitively express the different points of various operators in the process of image edge detection [9].

3.1.1. Roberts Operator

For the gradient magnitude of the detected pixel, the Roberts operator uses two 2 × 2 convolution templates at the detected point to calculate the current gray value, namely:

But the commonly used formula is as follows:

3.1.2. Laplace Operator

It belongs to the second-order differential operator of the detection algorithm, and the formula is as follows:

3.1.3. Sobel Operator

It can take into account all pixel points in the process of pixel grayscale calculation for the monitoring points of the graphics. Its basic formula is given by:

3.1.4. LOG Operator

It can overcome the shortcomings of the breeze and differential algorithms that are sensitive to noise. The commonly used formula template is given by:

3.1.5. Kirsch Operator

It has eight template operators, which will not be shown here. The most basic domain template of the checkpoint is as follows:

3.1.6. Prewitt Operator

It has the same idea as the Sobel operator, but its convolution template has changed in the selection of the convolution template, as shown in the following equation:

3.1.7. Image Gradient Edge Detection Algorithm

Its simplified calculation formula is given by:

3.2. Neural Network Algorithm

The information processing and computing ability of modern computers is strong, but in environments with different characteristics, its perception of things, its recognition of patterns in other things, and its logical approach to problems are not as good as those used by humans. It cannot do self-thinking and self-learning. It cannot be as flexible as the human brain; it can only rely on the programmers who developed it to input fixed formulas [10]. It has been known from early research that the working mode of the human brain is different from the working mode of the computer. The human brain consists of many basic units (that is neurons, as Figure 2) that are connected together to form a complex information processing system. This system is nonlinear and can process information in parallel. In fact, the efficiency of calculating the response in the human brain is bad, and it is not as high as that of a machine, but because the number of neurons in the human brain is large, the connections between neurons are also meticulous and the final consequence is that the human brain can process many problems, especially logic problems. It is much faster than the computer [11].

Figure 2

Schematic diagram of neuron composition.

Through the above understanding, people start from the perspective of imitating the neural configuration of human brain and use the characteristics of the brain's operating mechanism and organizational structure to find better methods, new models, and new information processing model that can simulate the processing methods of the human brain, that is, the artificial neural networks (NN) [12]. Generally speaking, a neural network is a model that uses optoelectronic components to imitate the intelligent processing process of the human brain and is simulated by computer software. As shown in Figure 3, the neuron model of its basic unit consists of three basic elements.

Figure 3

Basic neuron model.

The basic BP network algorithm contains two transmission places: forward and reverse. The specific meaning is that the mode of network transmission is calculated from the clockwise direction. However, the modification of each weight and threshold is carried out from the opposite direction as described above, as shown in Figure 4.

Figure 4

BP neural network structure.

The main learning algorithms of artificial neural network are error correction learning, Hebb learning and competition learning.

The actual output of the neuron at time m, the error can be written as:

The error correction learning means that the value of the objective function is reduced to the minimum range, then the output value will best match the output value of the sample. In real life, the more commonly used objective function is the mean square error criterion, which is defined as:

In order to get the whole system, formula (9) can be obtained:

Using the descent method, it can get the following equation:

Hebb learning is represented by a mathematical model as:

The most common cases are as follows:

In competitive learning, each output unit competes with each other, and only one can be activated in the end. The rule formula is as follows:

After calculation, the neural network formulas can be obtained as follows;

3.3. Ideological and Political Collaborative Education Mechanism

The understanding of the concept of the ideological and political education mechanism of university courses is generally considered by the academic circles to be an important course teaching method that uses all courses as a carrier to cultivate all-round development of students. However, this so-called concept of universal consensus obviously fails to fully explain the true meaning of the ideological education and political training mechanism in colleges [13]. According to the definition of curriculum education and the concept of collaborative education mechanism, this paper tries to understand the concept of education mechanism in colleges as using different courses as carriers, emphasizing the synergistic education function of each part, and the concept of education. It runs through the teaching practice of all courses, so as to jointly cultivate a teaching method [14]. Therefore, the ideological and political education mechanism in colleges and universities should be a mechanism with both coordination and sustainable development, emphasizing the mutual coordination and mutual promotion between the various subsystems of curriculum ideological and political.

3.3.1. Ideological and Political Lessons

Ideological and political lessons are a specific type of courses for college students to carry out ideological education and political training. It is the main force and the direction of the ideological and political construction of colleges and universities. The content of ideological and political courses taught to students is theoretical knowledge ground on the basic tenets of Marxism, which determines that ideological and political courses are the core content of ideological education and political training in colleges [15].

3.3.2. Professional Education Courses

Professional education courses are an important content and an important tool for the ideological and political construction of courses. It is the related work of ideological and political education through professional knowledge in professional curriculum education. The development of ideological and political construction based on professional education courses has an important promotion effect on solving the problem of “two skins” between ideological and political courses in colleges and other majors. In addition, while carrying out efficient ideological and political education for college students, it is necessary to deepen the teaching reform, increase the application of learning in this discipline, and further play the role of this major in educating people [16].

3.3.3. Comprehensive Literacy Course

Comprehensive literacy courses refer to public basic courses in addition to ideological and political lessons and professional education courses. It is one of the contents of the ideological and political construction of college courses. In the classroom teaching of comprehensive literacy lessons, schoolman can analyze the content of current events, pay attention to dissemination, and highlight the connotation and value of knowledge. This not only improve the ability of college students to learn knowledge but also teach students the strategies and potential skills of dealing with others and doing things, cultivate the healthy and good moral character of college students, and maximize the educational effect of comprehensive literacy courses [17].

3.3.4. Second Classroom

The second classroom refers to the education and tutoring practice outside the classroom in colleges and universities. Compared with classroom teaching, the second classroom in colleges and universities does not have a certain syllabus and requires the course to be completed within a specified time. It is more flexible and can be adjusted according to the actual needs of education and teaching, and its connotation is simple. Political courses, professional education courses, and comprehensive literacy courses are extensive [18].

4. Experiments on the Curriculum Establish of Ideological and Political Collaborative Education Mechanism Ground on Edge Computing and Neural Network Algorithm

4.1. Current Situation and Main Practices of Ideological and Political Implementation in a Domestic University Curriculum

At present, the transformation of ideological education and political training has been widely implemented in colleges across the country, and colleges and universities have common practices around policy requirements, and at the same time, they have their own characteristics based on the actual school conditions such as their majors and discipline characteristics. The diversification of courses does not mean the differentiation and specialization of ideological and political. Therefore, while summarizing the basic situation of the implementation of each school, it is essential to watch the decided implementation situation of a single school from the general situation of colleges and universities across the country, so as to take a closer look at the part through the overall situation, and then deepen the thinking about the curriculum of domestic colleges and universities from the partial inspection for accurate grasp of the overall situation of political implementation [19].

Taking the teaching reform in a university course as the object of observation and research, it mainly for the following considerations: a university has 24 undergraduate majors, and the establishment of majors and discipline training has involved medicine, science, engineering, management, law, seven disciplines such as pedagogy and literature. As a practical observation of course ideological and political teaching, colleges and universities have symbolic and universal significance. The curriculum system is complete. The understanding and specific practices are also representative in domestic colleges and can be used as a complete observation case [20].

4.1.1. Curriculum Ideological and Political Implementation Management Structure

According to the relevant policy documents of the school's ideological education and political training and the work arrangement at the meeting to put into effect of curriculum ideology and politics, the organizational structure of a university's curriculum ideological and political implementation management is as shown in Figure 5:

Figure 5

The ideological and political management structure of a university course.

The Academic Affairs Office is the management department put into effect of the school's organization and accepts the direct leadership of the school leaders in charge of teaching work. The guiding position of the Marxist College has been established, and the secretary of the Party Branch of the Marxist College is appointed by the school's party committee standing member, party committee propaganda department head, and party committee, minister of the United Front Work Department concurrently. The Party Committee Propaganda Department is the organizational department of the school's ideological education management and theoretical study [21].

And the university's secondary college courses also carry out project activities, for example in Table 1.

Table 1

The main project activities courses in a secondary college of a university

College	Time	Project activity name	The target
School of Marxism	2 019.11	Teaching reform of curriculumClass evaluation and feedback meeting	Advocating curriculum and political teachingSchool reform
H College	2 018.9	Curriculum teaching reform pilot course	The significance of the development of development of the seminar course,Connotation, method and so on, preliminary draft lessonCheng Sizheng education design scheme
J College	2 01812	Grasp the platform art to promote curriculum ideological politics -- JCollege young teachers training series of activities	Explore ideological and political elements, through a variety of podium art forms, through the professional tutoring process

4.1.2. “Ideological and Political” Elements in the Curriculum System

The professional disciplines of the university have involved 7 categories, and courses such as natural sciences, philosophy and social sciences and humanities are all offered. The school requires that all lessons must promote the implementation of the teaching reform. Due to the large number of courses, this article took the demonstration courses of 21 undergraduate courses established by a university in December 2019 as the analysis object. The main educational value of its “ideological and political” elements orientation is shown in Figure 6.

Figure 6

The proportion of “ideological and political” direction of 21 undergraduate “ideological and political” demonstration courses.

4.1.3. A University's Teachers and Students' Comprehension of the Real-Time Ideological and Political Situation of the Course

By distributing questionnaires to the group of the university, the teachers' and students' understanding of the meaning of the goals of the course and the recognition of the practice and effect of the schools have a good understanding of the teaching situation [22].

A total of 45 questionnaires were distributed to teachers, and 43 valid questionnaires were obtained. The subjects of the survey were full-time teachers of the university. The basic information of teachers is shown in Table 2.

Table 2

Basic information of teachers surveyed.

Basic situation	Project	Frequency	Frequency
Post	Professional course teacher	256	2 7.77
	Ideological and political coursemanagement staff counselor	406	44.03
		140	15.18
		120	13.02
Teaching age	0–5 _	456	49.46
	6–10 _	144	15.62 _ _ _
	11–15	96	10.41
	16–20	72	7.81
	20 +	154	16.7
Job title	Teaching assistant	261	28.31
	Lecturer	313	3 3.95
	Associate professor	136	14.75
	professor	98	10.63
Other		114	12.56

Then, the results are shown in Figure 7. More than 95% of professional teachers believe that teachers' daily teaching behavior and process will have an impact on students' world outlook, outlook on life, and values; 48.84% of teachers think it is important to integrate ideological and political elements in the courses they teach, and 46.51% of teachers think it is more important. From these two points, it can be seen that the professional course teachers of the university fully agree with the necessity of course teaching [23]. Teachers' understanding of the content and value of the elements in the curriculum elements are mainly academic integrity, professional ethics, scientific spirit, and the fourth-ranked love for the party and patriotism. It can be seen from the figure that professional ethics and scientific spirit are higher than other elements of the lessons, that is, more than 60% of teachers regard professional ethics and scientific spirit as the first elements of curriculum ideological and political integration. It is easy to find that most of the teachers in the campus's professional course teachers think that the course ideology and politics include patriotic education, but professional ethics and scientific spirit are often placed first in value education.

Figure 7

A university's professional course teachers' understanding of ideology and politics.(a)Teachers' understanding of the ideological and political elements in the ideological and political courses of a university (b)The ranking of the first degree of the ideological and political elements in the ideological and political courses of the professional course teachers of a university.

At the same time, a survey was also conducted on the students. The basic information of the students' survey is shown in Table 3.

Table 3

Basic information of the students surveyed.

Basic situation	Project	Frequency _	Frequency
Education	College students	72	7.59%
	Undergraduate	721	77.97%
	Postgraduate	148	15.6%
	PhD student	8	0.84%
Political status	Communist Youth League	702	75.03%
	Communist party members	151	15.91%
		5	0.53% _
	Democratic party the masses	81	8.54%
Professional category	Science and technology	501	52.79%
	Social science	176	18.55%
	Humanities	198	2 0.86%
	Art	74	7.8%

The survey results are shown in Figure 8. First of all, for the relationship between “morality” and “talent,” 56.47% of students believed that the impact of personal morality on the future development of college students was the first, and 38.79% of students believed that personal talent was the most important. The proportion of family background factors was low, indicating that the value pursuit of the college students was generally positive. When it comes to the “virtue” of building morality in higher education, students' understanding of the meaning of “virtue” is shown in Figure 8. More than half of the students believe that the “virtue” of building morality and cultivating people is mainly civic morality, and about 30% of the students believe that the main value is love for the party and patriotism, that is, most students think that the virtue of building morality and cultivating people is to establish morality.

Figure 8

Questionnaire results of current students (a)Investigation on the importance of personal development factors of students in a university.(b)A university student's understanding of the meaning of “virtue” in establishing morality and cultivating people.

4.2. Building of Ideological and Political Collaborative Education Mechanism Ground on Edge Computing and Neural Network Algorithm

Construction of the ideological and political collaborative education mechanism is a complex and systematic process although the current situation of ideological and political is analyzed above. In fact, using a linear function to construct the model in this article cannot achieve the desired effect because the effect of each specific factor in the model cannot be estimated. This may be nonlinear to the resulting final result. So a method that can determine the model created in this paper is needed to produce a model that predicts in advance [24, 25]. At this time, artificial neural networks have entered world's field of vision.

4.2.1. Establishment of Sample Set Data

Data preparation is crucial for artificial neural networks. A great data can rapidly make the network converge to a balance [26-28]. The input data of this network system come from the questionnaire compiled.

But to ensure the fitness of the BP network neural system model, this paper compared it with other models [29-31], as shown in Table 4.

Table 4

Comparison of different models.

Model	Accuracy	The recall rate	F1 value
LSTM	83.17%	8 4.12%	83.64%
Bi-LSTM	86.22%	85.62%	85.92%
BiLSTM-CRF	89.65%	88.72%	89.18%
B p	90.01%	89.33%	89.23%

Through the experiment, it can be known that the BP model is more suitable for this experiment [32]. But the selection of nodes is also important, so experiments with different nodes have been conducted, and the results are shown in Table 5.

Table 5

Experimental results for different nodes.

Number of nodes	Accuracy	The recall rate	F1 value
50	80.24%	81.89%	81.13%
100	89.65%	88.70%	88.13%
150	78.64%	80.13%	79.23%
200	77.45%	85.23%	89.23%

The generalization degree of diverse nodes is also diverse; this time the number of nodes can be selected as 100.

For the study of structural input and output layers, its main function is to input data and then get the final processing result. Under normal circumstances, the number of outputs of the input layer of the network is basically the same as the number of inputs of the problem to be processed. The number of neurons in the input layer used in this network model is 10, and the number of neurons in the output layer is 1.

Preprocessing of neural network data: in order to make the training of the neural network more effective and improve the training speed of the established neural network, the input and output data of the neural network must be preprocessed first. The preprocessing methods provided by MATLAB include normalization, standardized processing, and principal component analysis. The most commonly used method is normalization, and then after the training is completed, the method is reversed to the original data range. Again, the data should be shuffled and classified.

4.2.2. Network Training

Before exercise, various parameters of network training were set. After many experiments, the number of network training was finally set to 1000, the training target was set to 1e − 6, the learning rate was set to 0.01, and the default conditions were used for other network parameters. In order to ensure the accuracy of the experiment, two experiments were done to ensure the error, as shown in Figure 9. From the figure, the network performance converges well and can meet the needs.

Figure 9

Neural network model error plot. (a) The first neural network training process. (b) The second neural network training process.

The performance of the network state was also different, as shown in Figure 10.

Figure 10

Network training state. (a) Network training state 1. (b) Network training state 2.

4.3. Countermeasures to Strengthen the Construction of Curriculum Ideological and Political Collaborative Education Mechanism

After the above study, the construction of the current collaborative education mechanism in colleges and the research based on spiritual network and edge computing, the countermeasures to enhance the establishment of the curriculum ideological education, and political training mechanism can be drawn.

4.3.1. Strengthening Thought Leadership

Strengthening the learning meaning of ideological education and political training reform in colleges is a concrete demonstration of the Sinicization of the basic theory of Marxism in the ideological education and political training work of colleges and universities. The premise of strengthening the ideological education and political training construction of colleges and universities is to strengthen ideological guidance and continue to do a good job in the study of the educational concept of ideological education and political training construction of lessons. The study of educational concepts should effectively make all participants in the ideological education and political training construction of courses realize the importance and urgency of the ideological education and political training construction of college courses from the height of politics.

4.3.2. Improving the Collaborative Education Mechanism of Colleges

Aiming at the matter of the imperfect system and mechanism of ideological education and political training collaborative education in various professional lessons, this paper tries to propose to build an ecological system of ideological education and political training collaborative education of various professional lessons and unblock the communication channels of ideological education and political training collaborative education of various professional lessons. The following descriptions will be made one by one in the abovementioned order.

4.3.3. Increasing the Guarantee of Funds, Technology, Personnel, etc

The previous research showed that there were gaps in funds, technology, and teachers in the process of ideological education and political training construction in colleges and universities. This paper attempts to propose to promote the ideological and political construction of college teachers' curriculum from the two aspects of “improving the institutional system, strengthening financial and technical support,” and “establishing a normalized training mechanism and increasing the intensity of teacher training”.

4.3.4. Optimizing the Evaluation System of Ideological Education and Political Training Construction in Colleges

The previous research showed that the evaluation standards and systems for the ideological and political construction of high-efficiency courses were not perfect, mainly for the effectiveness of the ideological education and political training construction of the curriculum and the evaluation of education and teaching. The follow-up will be analyzed in accordance with the above sequence, and it is recommended that colleges and universities introduce corresponding systems and measures to detail and implement the evaluation of curriculum ideological education and political training construction.

4.3.5. Actively Responding to the Potential Impact of the External Social Environment

The previous research found that the single pursuit of economic development and multiculturalism had a certain negative impact on the ideological education and political training construction of college lessons and suggested that colleges and universities should plan in advance to actively respond to the potential impact of the external social environment. In the follow-up, strategic suggestions will be put forward from two aspects: establishing a reasonable and standardized risk prevention mechanism and opening relevant courses to strengthen positive guidance.

5. Discussion

This paper focused on the construction of the ideological education and political training collaborative education mechanism based on edge computing and neural network arithmetic and applied it to the complex analysis and processing of ideological education and political training lessons in colleges and universities. It not only expands the application scope of edge computing and neural network algorithm but also is a new attempt to study the complexity of the curriculum ideological education and political training collaborative education mechanism. Through the analysis of edge computing and neural network algorithm, the two algorithms were used as an important tool to study the complexity of the system. For the research on edge computing and neural network, this paper started with the most basic typical representative formula and successfully combined the creation of the curriculum ideological education and political collaborative training mechanism with the BP neural network model and drawn conclusions. In the stage of empirical analysis, the model was used to study the construction of the curriculum ideological education and political training collaborative education mechanism and was analyzed in many aspects.

It is shown that the research on the construction of the curriculum ideological and political collaborative education mechanism ground on edge computing and neural network algorithm is more effective. By analyzing the data obtained by establishing the BP neural network model, the curriculum ideological education and political collaborative training can be better put into effect. In the specific practical decision-making, colleges and universities formulate corresponding ideological education and political training collaborative education mechanism construction countermeasures according to the most effective data and methods.

This article studied the construction of the ideological education and political training collaborative education mechanism of a university course. First, through the questionnaire survey and qualitative analysis, the current situation of the university was determined, and the edge algorithm and the neural network algorithm were used to construct the model. Through the analysis of the data, the countermeasures for the establish of the curriculum ideological education and political training collaborative education mechanism were obtained.

6. Conclusions

Through the case study, an important conclusion is drawn: generally speaking, the construction of the curriculum ideological and political collaborative education mechanism should be based on five aspects, that is, the drainage of ideas; the improvement of the education mechanism; and the guarantee of funds, technology, personnel, and other aspects; curriculum ideological and political construction evaluation system and external impact solution. But this is not absolute, such as the study of ideological and political courses in a certain school in this case. This requires more detailed research and quantitative analysis to determine a more effective curriculum ideological and political collaborative education mechanism construction countermeasures. The project discussed in this paper is to determine the construction of the curriculum ideological and political collaborative education mechanism on the basis of the combination of edge computing and neural network algorithm, and the selection of projects is limited, while domestic colleges and universities often get more choices. In reality, the construction of curriculum ideological and political collaborative education mechanism should also be studied and analyzed in combination with multiple factors, which will have greater value and greater difficulty. But what is worth looking forward to is that the attention of domestic curriculum ideology and politics has been increasing, and there are more and more studies on the construction of ideological and political collaborative education mechanisms. It can always be believed that in China, the construction of the ideological and political collaborative education mechanism in the curriculum can get better and better.