Entrepreneurial Mentality Analysis for College Students Based on the Improved BP Neural Network.

In this study, the fuzzy comprehensive evaluation model is used to evaluate the characteristics of college students' entrepreneurial psychology, and a prediction model of college students' entrepreneurial psychology characteristics is established, which is simulated by Matlab to achieve good validity. Based on the research on the characteristics of college students' entrepreneurial psychology, this study proposes a design method of indicators and parameters for evaluating the characteristics of college students' entrepreneurial psychology. In this study, the genetic algorithm is used to optimize the BP neural network. The optimized neural network greatly improves the global search and local search capabilities. The performance of the model is tested through simulation tests. Through the simulation comparison test between the improved model and the standard model, the results show that the model can predict the entrepreneurial psychological characteristics of college students. By comparing the improved BP neural network algorithm with the original algorithm simulation experiment, the improved BP neural network improves the sensitivity by 20%, the specificity by 5%, and the accuracy by 8%.

1. Introduction

According to the relevant survey data of the “Blue Book on the Development of Innovation and Entrepreneurship Education in Chinese Universities,” the number of college students and graduates engaged in entrepreneurship has continued to grow, from 2.8% in 2014 to 4.6%, and the proportion of students interested in entrepreneurship is close to 92% [1]. However, college entrepreneurship instructors (35.8%) and college student entrepreneurs (25.7%) both reflected that the lack of professional entrepreneurial service guidance is the main dilemma in the current innovation and entrepreneurship education [1]. The contradiction between the high entrepreneurial enthusiasm and the lack of professional guidance requires us to further study what kind of professional guidance college students need for entrepreneurship? What is the psychological motivation of entrepreneurial college students? Are there significant differences in personal psychological traits between entrepreneurial college students and nonentrepreneurial college students?

In the previous studies on the characteristics of college students' entrepreneurial psychology, questionnaires and scale tests were mostly used (such as Sexton's Dilemma Questionnaire and Occupational Value Scale, Kourilsky's systematic follow-up research, etc.) [2-4]. The combined method extracts the mentality of college students' entrepreneurs from the perspective of sociology and determines the individual characteristics by comparing them with nonentrepreneurs. These studies have theoretically proved that the entrepreneur group does have characteristics different from other groups, and believes that such literacy can be cultivated through education. Although such studies and conclusions have already involved the analysis of multiple personality traits, they are more based on psychological tools for testing but have not yet formed a unified definition and conducted an effective demonstration. The proposed mentality has not been demonstrated and summarized using mature theoretical tools. The prediction of college students' entrepreneurial mentality is a complex and systematic process. Although a model of college students' entrepreneurial mentality is established in this study, which consists of five feature sets, including 22 factors, it is not simple to use a linear function to establish a prediction model of college students' entrepreneurial mentality, because each specific factor plays a role. The process is invisible and intangible, and the impact on the results may be nonlinear. Therefore, a method that can reflect the corresponding relationship between factors and results of entrepreneurial mentality is needed to establish a predictive model. At this time, artificial neural networks entered our field of vision.

An artificial neural network is a mathematical model for information processing that is connected by synapses similar to the structure of neurons in the brain. The corresponding relationship between the input data and the output data is automatically found through the training method, and the learning process does not need human control, just adjusting the training parameters, which provides us with a very practical modeling means. Among them, the backpropagation neural network is a multilayer perceptron structure model, which consists of several layers of neurons. The BP neural network algorithm turns the input and output problems of a set of samples into a nonlinear optimization problem of the network, uses the iterative method to solve the weight problem of learning and memory, and obtains more accurate values by increasing the hidden layer node parameters [5]. Based on the evaluation model of college students' entrepreneurial mentality established in this study, and using the genetic algorithm to improve the BP neural network, a prediction model of college entrepreneurial mentality is established, which greatly improves the global search and local search ability of the model for sample characteristics. After training and testing the model, the improved BP neural network has higher accuracy in predicting the characteristics of college students' entrepreneurial psychology.

2. BP Neural Network

BP neural network is a feed-forward network with backpropagation. Its basic idea is that through continuous learning of sample data, the neural network uses the adaptive ability to adjust the error of the weight threshold of each neuron in the network so that the error function is adjusted in the direction of gradient descent. Each neuron in the network is like a synapse of the human brain [6]. By reasonably dividing the complex information, it adjusts the relationship between its own input data and output data. Among them, there are activation functions for both the hidden layer neurons and the output layer neurons. The activity of individual neurons is adjusted [7].

2.1. Learning of the BP Neural Network

The feedback network continuously adjusts its own network through the learning and practice of sample data and finds out the mapping relationship between input and output through reverse adjustment of errors [8]. After training, the network can make correct judgments for the same type of input. The network is to combine each neuron, layers the neurons, and thereafter the layered neurons receive the information input of each neuron, and at the same time process the information and output it to other neurons [9]. Neurons have both input and output and form a network by continuously adjusting the connection weight threshold. The program flow of the BP algorithm is shown in Figure 1 [10].

Figure 1

BP algorithm program flowchart.

Step 1: read the sample data and perform data normalization processing, and normalize the data to the interval of [−1, 1];

Step 2: build a network, design the structure and parameters of the network, initialize the weight threshold of each neuron, and randomly select the number in the [−1, 1] interval;

Step 3: calculate the input and output of each layer of neurons;

Step 4: the error between the network output and the expected output is fed back layer by layer;

Step 5: according to the obtained error function, it is fed back to the output layer in layers, the partial derivatives of each neuron are calculated, and the weight threshold of each neuron is corrected;

Step 6: the error function is fed back layer by layer to the connection weight between the output of the hidden layer and the input of the output layer, and the weight threshold is corrected according to the partial derivative calculated for each neuron in the hidden layer;

Step 7: calculate the global error;

Step 8: if the error reaches the preset accuracy of the network or the network fails to reach the network accuracy within the specified number of learning steps, the training ends; if not, the network selects the next sample to perform the same steps of learning until the end of the training [11].

2.2. Defects of the BP Algorithm

In general, the BP neural network has a sufficient theoretical basis, and the algorithm derivation is rigorous and logical. The formula for solving the partial derivative of the error function to each neuron is very mathematically beautiful, and the neural network can be applied in all directions. The BP algorithm is a process of continuously seeking local optimum based on the error function, so there are inevitably the following four major defects:

The network is easy to fall into local minima. The goal of the BP algorithm is to minimize the error function, and this algorithm is prone to extreme phenomena in the process of optimization. In solving nonlinear complex problems, the design of the number of network layers and the number of neurons is different, and the error function of each network is different. Single caught in an extreme phenomenon.

Slow convergence [12]: when building a BP neural network, some parameters will be adjusted with the training of the network, but some parameters are fixed. The BP algorithm essentially achieves the goal of minimizing the error function by continuously adjusting the weight threshold, but such requirements are in Slows down the convergence of the neural network.

The determination of the network structure is controversial. For different objective functions, the structure of the neural network is different. Usually, in order to solve practical problems, the selection of the structure of the network is based on the method of empirical attempts, and the theoretical basis is relatively weak, so the establishment of the network structure and the network. The performance can only be tested by practical application [13].

The generalization ability of the network cannot be determined. The generalization ability of the BP neural network is affected by many factors, and each influencing factor is intertwined, which affects the generalization ability of the neural network.

In practical applications, the advantages and disadvantages of the BP neural network coexist, and the development of the BP neural network in all aspects is limited to a certain extent. Facing practical problems, it is necessary to optimize the BP neural network in a targeted way [14].

2.3. Genetic Algorithm

J. Holland first proposed the concept of the genetic algorithm in 1975, and after summarizing it, the genetic algorithm was developed in the late 1980s [15]. The algorithm draws on the evolutionary laws described in the theory of biological evolution and can randomize the global search. Similar to the neural network algorithm, when the problems faced are different, the individual coding method, fitness function, and various operators of the genetic algorithm are different, but the essence of all genetic algorithms is the same, that is, the population is carried out according to the fitness function. Selection, mutation, and crossover operations generate new populations through operators. The newly generated populations have higher fitness than previous generations of populations and complete the global search for the optimal population, as shown in Figure2.

Figure 2

Genetic algorithm flow.

In the genetic algorithm, different parameters are set for different problems. When facing the problem of optimizing the neural network, the following elements need to be considered [16].

2.3.1. Coding Method

① Binary code

Binary encoding is the best encoding form of the 0–1 knapsack problem. If the decision variables [x1, x2,…, x] are represented by L(L=n∗l) bit binary strings b1, b2,…, b (variable x is Expressed as b, the variable is encoded as a 0–1 string, and its length is determined by a given constant l.

② Floating-point encoding

Floating-point number coding is a coding method that uses floating-point numbers in a certain interval to represent individual genes. It can solve complex numerical problems, and its length is determined by the number of unknowns.

2.3.2. Construction of the Fitness Function

In the genetic algorithm, the size of the individual's survivability is described by the individual's fitness, and the fitness is the degree of conformity to the problem target. Through the iterative process of the algorithm, the individual with the largest fitness value is the optimal solution. The fitness functions mainly include [17]:

If the objective function is a minimum value problem,

If the objective function is a maximum value problem,

C is the given smaller number and max C is the given larger number. According to the specific problem, choose the appropriate fitness function.

2.3.3. Design of Selection Operator

The selection operator is an operation for screening individuals in a population, which can effectively reduce the calculation time, improve the global convergence, and avoid the occurrence of missing important genes.

Proportional selection operator

The basic idea of the proportional selection operator is that the higher the fitness of the group, the greater the probability of being selected, that is, the relationship between the probability iP of individual i being selected and the fitness F is:

Design of crossover operator

For the same population, the parent chromosomes are recombined and crossed to generate new chromosomes, which is also an important process for new organisms in biological evolution

One-point crossover determines the location of a crossing point and places new creatures behind this point after spawning

Two-point crossover determines the position of the two crossover points and arbitrarily exchanges this part of the gene

Arithmetic cross-over, two parent chromosomes and weighted average produce two daughter chromosomes. If the parent is X, X, the formula is

2.3.4. Design of Mutation Operator

① Basic bit variation

Mutation method for binary encoding

② Uniform variation

Uniform mutation, within a range, randomly selects a chromosome as a mutation point and has a small probability to replace the original gene. The genetic algorithm is applied to the neural network, that is, the optimal solution is assigned to the connection weights and thresholds of each factor of the neural network, and then the weights and thresholds are adjusted through the neural network [18]. The optimized neural network process is shown in Figure 3. The weight threshold optimized by the GA algorithm can be closer to the value required by the network.

Figure 3

Flowchart of the optimized BP algorithm.

3. Analysis on the College Students' Entrepreneurial Mentality

The object of this study is the individual college students and entrepreneurial team groups who have started their own businesses in school and whose entrepreneurial projects have been successfully operated for more than one year. Entrepreneurial mentality refers to the stable and individual mentality that regulates the psychology and behavior of college student entrepreneurs in the process of entrepreneurial practice. After defining the above two core concepts, this section mainly analyzes the mentality of college students who have successfully started a business.

According to the homogeneous sampling principle of qualitative research [19], this study selects eight maker spaces as research sites. A total of 24 teams were selected as the research object and the main body of the entrepreneurial team is college students. Through individual interviews and focus group interviews, the mentality of the entrepreneurial team is analyzed. In the interviews, open-ended, specific, and clear questions are designed, and audio and video recordings are used to record the original data truthfully. The NVIVO 11 for Windows software is used to classify and analyze the interview results. The code and subcategory frequency analysis is given in Table 1.

Table 1

Mentality of college student entrepreneurs.

Codes and categories	Frequency	%	Number of people	%
Entrepreneurial expectations	110	20.83	21	87.5
Enthusiasm	34	6.44	13	54.17
Interest	23	4.35	19	79.17
Confidence	23	4.35	24	100
Support from family and friends	12	2.27	9	3.75
Diligent	18	3.41	15	62.5
Opportunity recognition	92	17.43	23	95.83
Seize the opportunity	26	4.92	21	87.5
Continue to innovate	24	4.55	20	83.33
Access to information	18	3.41	23	95.83
Action	24	4.55	22	91.67
Team integration ability	125	23.67	24	100
Leadership	32	6.06	23	95.83
Teamwork	25	4.73	22	91.67
Sense of responsibility	24	4.55	20	83.33
Persistence	31	5.87	21	87.5
Communication	13	2.46	14	58.33
Risk prevention ability	107	20.27	18	75
Antifrustration	24	4.55	12	50
Actively correct	16	3.03	18	75
Choice	20	3.79	14	58.33
Adventurous	30	5.68	18	75
Calmness	17	3.22	14	58.33
Entrepreneurial value orientation	94	17.80	16	66.67
Professionalism	21	3.98	20	83.33
Career plan	12	2.27	11	45.83
Active thinking	28	5.30	14	58.33
Sense of achievement	33	6.25	16	66.67
Total	528	100	24	100

After 32 interviews, 528 samples of individual mentality were established, and the interview results were integrated by coding classification. This study divides the entrepreneurial mentality of college students into five mentality sets. Each feature set has several factors. In order to ensure that the survey data on the characteristics of college students' entrepreneurial psychology are used in the prediction model, this study uses vectorization to characterize the characteristic factors, so that the specific results can be transformed into mathematical methods. The specific methods are as follows.

The five feature sets can be expressed asand the representation of each feature set is given in Table 2.

Table 2

Classification form of feature set.

Category	Representation
Entrepreneurial expectations	f 1
Opportunity recognition	f 2
Team integration ability	f 3
Risk prevention ability	f 4
Entrepreneurial value orientation	f 5

All factors are given in Table 3.

Table 3

Representation of all factor sets.

Category	Factor sets	Representation
Entrepreneurial expectations	Enthusiasm	F 1
	Interest	F 6
	Confidence	F 11
	Support from family and friends	F 16
	Diligent	F 21
Opportunity recognition	Seize the opportunity	F 2
	Continue to innovate	F 7
	Access to information	F 12
	Action	F 17
Team integration ability	Leadership	F 3
	Teamwork	F 8
	Sense of responsibility	F 13
	Persistence	F 18
	Communication	F 22
Risk prevention ability	Antifrustration	F 4
	Actively correct	F 9
	Choice	F 14
	Adventurous	F 19
	Calmness	F 23
Entrepreneurial value orientation	Professionalism	F 5
	Career plan	F 10
	Active thinking	F 15
	Sense of achievement	F 20

The established secondary evaluation matrix is

4. BP Neural Network-Based Modeling of Entrepreneurial Mentality Analysis

By quantifying the main factors affecting the entrepreneurial psychological characteristics of college students, we can predict the entrepreneurial psychological characteristics of college students. Data prediction is a very important task in data mining. It is widely used in various fields. The direct mapping between data and ideal output is described by establishing a mathematical model [20]. A brief description of the method is as follows:

Training: training samples > self-learning function > training > prediction model.

Prediction: test sample  > feature selection > prediction > result comparison.

4.1. Criteria for Evaluating Prediction Methods

Accuracy: it refers to the ability of the model to correctly predict unknown data categories. It is a relatively widely applicable comparison scale when dealing with predictive classification tasks;

Computational complexity: the implementation platform of the algorithm and the device configuration will affect the computational complexity. Since the operation object is a huge amount of data, in the process of data mining, a very important link is the analysis of space and time dealing with the complexity of the problem;

Robustness: it refers to the fact that the model can still make correct predictions, despite data loss or data with noise;

Scalability: it refers to the ability to process large amounts of data and construct corresponding effective models;

Simplicity of model description: the description of the model is often more inclined to the model with high simplicity, which is easy to understand and more practical

4.2. Establishment of the Prediction Model

Through the research on the entrepreneurial group of college students, the testees quantified the influence of 14 main influencing factors on the entrepreneurial mentality according to their own situation (Likert 5-point scoring method), and the input data were the results obtained by quantification. Using the corresponding entrepreneurial mentality (the results judged by the fuzzy comprehensive model) as the output data, a sample set is established. A three-layer neural network model is constructed, and the hidden layer is adjusted to optimize the accuracy of the network. The sample data is randomly divided into two parts, one part is used as a training sample to train the network, and the other is a test sample, that is, the trained network is tested to determine whether the trained network meets the requirements. Finally, the model is simulated and tested to judge whether the prediction model can achieve the expected prediction purpose. Using the probabilistic adaptive iterative optimization ability of the genetic algorithm, the given fitness function is used to find the most suitable chromosome with the greatest probability, and its global optimization ability does not depend on the gradient information.

The genetic algorithm is used for the BP network. The optimization of the weight threshold is carried out, and the search range of the weight threshold is expanded. The optimal weight threshold found by the genetic algorithm will replace the initial weight threshold randomly generated by the BP neural network, which narrows the search range of the original algorithm. To a certain extent, the computational complexity of the BP algorithm is reduced. Through the effective combination of these two algorithms, the complementary advantages of the two algorithms are achieved. The optimized neural network not only improves the learning speed but also greatly improves the approximation ability of the network during the whole network training process, and improves the generalization ability of the network. The optimized BP neural network algorithm is shown in Figure 3.

4.2.1. Design of Network Structure

Network layer design

The input layer has 14 neurons, Y represents the input data of the jth node of the network input layer, where j=1,…, 14 ;

The hidden layer has 14 neurons: the empirical formula for the number of hidden layers in a neural network is

The number of neurons: Y, the number of neurons in the output layer: O, a is a constant, and the number is h=8+ a, according to the empirical formula. Based on the above situation, this study adopts the trial method to obtain the number of neurons in the hidden layer: when 5–20, the upper limit of the number of iteration steps is set to 100, and the preset target is 0.01. For the BP neural network and the optimized BP neural network, carry out network training; the results show that the optimized neural network is improved compared with the preoptimized neural network in terms of operation step size, operation time and the efficiency of reaching the preset target, and when the number of neurons is 14, the optimized neural network has the strongest ability to fit the data. The upper limit of the number of iteration steps is set to 1000, and the network is retrained, respectively. It can be seen from the network training that when the number of hidden layers is 14, the neural network and the best fit to the data are achieved. Therefore, the number of hidden layers of the prediction model is set to 14.

The output layer has 1 neuron: entrepreneurial mentality obtained through fuzzy comprehensive evaluation: the output of the kth node of the network output layer: o, this study uses o to represent the output. The activation function of the output layer of the network is ϕ (x) : transig.

Weights and thresholds:

The connection weight w from the second layer neuron i to the first layer neuron j

The threshold value of the neurons in the second layer is θ

The connection weight w from the third layer neuron k to the second layer neuron i

Threshold a for neurons in the third layer

The goals of the model are

The trained neural network can approximate the ideal output through the new input, that is, the error between the actual output and the ideal output is the smallest.

4.2.2. Network Parameter Selection

(1) Initialization of Parameters. On the Matlab platform, use newff to create a network. The initff function is an initialization function. The created new network automatically calls this function to initialize the network. The so-called network initialization is to initialize the parameters of the network and give the parameters of the network. Default value (default value).

(2) Training Function. In practical applications, the selection of the training function will also affect the data fitting degree of the entire network. The traditional trainbp algorithm has a slow training speed in practical applications, and it is difficult to achieve the desired effect. It selects the trainlm training function which is better than the traditional algorithm, and has a strong data fitting ability for medium-scale BP neural network. Since the solution of the second-order Hessian matrix is avoided, the convergence speed of the network is improved to improve the fitting degree of the network.

4.2.3. Selection of Genetic Algorithm Operators

(1) The Coding of Chromosomes. Since binary encoding has the process of encoding and decoding, which affects the space and time complexity of the algorithm, this study uses the real number system to encode.

(2) Construction of the Fitness Function. In the previous article, the error function of the neural network is defined. The error function is also the objective function of the network. The fitness function in this study is the derivative of the objective function:

(3) Design of Selection Operator. Based on the construction of the fitness function, the proportional selection operator is more suitable for the situation in this study. In M populations, the probability P of the individual i with F being selected is

(4) Design of Crossover Operator. Real number coding is more suitable for arithmetic crossover. After the arithmetic crossover of two parent individuals X and X, two offspring individuals are generated:

(5) Design of Mutation Operator. For the mutation of the population X=x1x2 … x … x, the value range of the mutation point x is set as: [UminUmax], through the mutation operation on X, the chromosome of the mutation point in the new individual X′=x1′x2′ … x′ … x′ changes:

(6) Combination with BP Algorithm. After the training of the genetic algorithm, the population with the largest fitness is decoded, and the decoded population is in the neural network to be solved, and the connection weight threshold with the smallest error function is reached, and it is assigned to the neural network. The weight threshold is adjusted until the output satisfies the end condition.

5. Experimental Studies

5.1. Data Acquisition

According to the survey data on the characteristics of entrepreneurial psychology of college students in the third chapter as the data set of this research, the influence of 23 main influencing factors on their own mental health status was quantified (Likert 5-point scoring method), as the input of the sample data. Through the prediction model of college student entrepreneurs' psychological characteristics established in this study, the prediction results obtained are used as the output data of the BP neural network prediction model.

5.2. Analysis of Results

The neural network model constructed in this study has an accuracy of 84.56% and 84.02% during training and testing, respectively. The sensitivity and specificity of the training set were 90.00% and 79.12%, respectively, the Youden index was 0.69, and the Kappa coefficient was 0.69; the sensitivity and specificity of the test set were 90.00% and 78.05%, respectively, the Youden index was 0.68, and the Kappa coefficient was 0.68; the model The AUC of 0.890. The training set and test set prediction results are given in Table 4.

Table 4

Analysis of the prediction results of the improved BP neural network model.

Group	Forecast vs. actual		Accuracy (%)
	Y	N
Training set	144	27	84.56
Test set	68	13	84.02

The prediction results of the improved BP neural network model and the unimproved BP neural network are compared. According to the detection results of the two models, the improved BP neural network is superior to the original BP neural network in all indicators. The network model has better psychological characteristics for entrepreneurs. The main performance evaluations of the two prediction models are given in Table 5.

Table 5

Index evaluation of two prediction models.

Category	Sensitivity (%)	Specificity (%)	Youden index	Kappa coefficient	Accuracy	AUC
Improved BP neural network	90	82.58	0.68	0.68	84.02	0.890
BP neural network	70.83	78.05	0.53	0.54	76.7	0.826

The improved BP neural network model has better prediction performance for college students' entrepreneurial psychological characteristics. Through the application of this model, it can help college students to predict psychological characteristics before starting a business, improve weak links, and increase the success rate of entrepreneurship. By comparing the improved BP neural network algorithm with the original algorithm simulation experiment, the improved BP neural network improves the sensitivity by 20%, the specificity by 5%, and the accuracy by 8%.

6. Conclusion

In this study, the fuzzy comprehensive evaluation model is used to evaluate the characteristics of college students' entrepreneurial psychology, and a prediction model of college students' entrepreneurial psychology characteristics is established, which is simulated by Matlab to achieve good validity. Based on the research on the characteristics of college students' entrepreneurial psychology, this study proposes a design method of indicators and parameters for evaluating the characteristics of college students' entrepreneurial psychology. According to the model requirements, the neural network is optimized, and the BP neural network is optimized by the genetic algorithm. The optimized neural network greatly improves the global search and local search capabilities. The performance of the model is tested through simulation tests. The prediction of the entrepreneurial mentality of college students can be achieved to a certain extent, and the actual comparison test between the established model and the standard model is carried out. The results show that the prediction accuracy of the improved BP algorithm is improved by 10% compared with the original algorithm, and other evaluation parameters are improved.