# Effectiveness Analysis of Entrepreneurial Method with Computer Data Simulation

Authors: Yuanjun Shao
Journal: Security and Communication Networks
 (2022)

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## Abstract

Entrepreneurship has permeated all parts of society as a result of society’s development. College students are the main force of today’s entrepreneurship, and this work takes college students as the object of research. College students should focus not only on quantity but also on quality. In recent years, college students have engaged in a greater number of entrepreneurial activities, although their efficacy and influence have been limited. To improve the effectiveness of college students’ entrepreneurial methods, it is necessary to analyze their entrepreneurial methods to evaluate their effectiveness for method. This work presents a neural network for evaluating the efficacy of entrepreneurial strategies and employs computer data simulation technologies to validate effectiveness using a neural network and computer data simulation. First, using characteristics of global optimization of particle swarm optimization (PSO), a dynamic domain population model is proposed to improve it. The new model is with a K-means clustering algorithm and aims to increase the diversity of the population. The enhanced PSO (KPSO) algorithm has a better global optimization ability, thus particles are less likely to fall into local extreme values. Second, maintain consistency between the particle dimension size in KPSO and the weights and thresholds in the BP network and create a mapping link between KPSO and the weights and thresholds. After determining the topology for the neural network, the improved BP network (KPSO-BP) is utilized to analyze the effectiveness of college students’ entrepreneurial methods. Third, the computer data simulation results verify the effectiveness and correctness of the proposed method.


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## Body

## 1. Introduction

Entrepreneurship and the personal quality of the entrepreneur are directly related to the future direction planning of the entrepreneur. Because of the state’s emphasis on entrepreneurship, tougher standards and more stringent requirements for top-tier talent have been established, and entrepreneurship among college students has received significant support and direction from policymakers. The future of a country and a nation is in the hands of its young people, who are the lifeblood of technological advancement and the engine driving social growth. The entrepreneurial spirit thrives in the minds of today’s youngsters, who have boundless energy, imagination, and drive. There is hope for the country, and there is hope for the country’s future, in the youth. The government mandates that young people be assisted in resolving their issues and obstacles in finding work and starting their own businesses once they have graduated. With the development trend of innovation, colleges and universities not only shoulder the task of scientific and technological innovation but also shoulder the important task of cultivating high-quality entrepreneurial talents and improving the quality of college students’ entrepreneurship [1–5].The research pointed out in detail that in 2020, the number of graduates from colleges and universities will reach 8.74 million. State and municipal governments have adopted numerous relevant support policies for college students’ employment and entrepreneurship, and colleges and universities have also done specific work to improve employment and entrepreneurship. The entrepreneurial form of college students is not optimistic. First of all, from the perspective of the proportion of college students starting their own businesses, the proportion of college graduates who started their own businesses in 2018 was 2.7%, which was slightly lower than that in 2014. According to the comparison between the 2018 data and the previous data, the entrepreneurship rate of the 2018 graduates dropped by 0.2% compared with the 2014 graduates, and only 2.7% of them started entrepreneurship. Among them, the proportion of self-employment of fresh graduates of higher vocational colleges is slightly higher than that of undergraduates. The entrepreneurship rate of the 2015 graduates reached 6.2% within three years after graduation, and the rate of undergraduate graduates was much lower than that of vocational college graduates. Graduates in 2015 had a 44.8% survival rate for self-employment, which is lower than the 2014 survival rate of graduates in self-employment. This shows that college students’ entrepreneurship is still in need of improvement, as the rate of entrepreneurship has decreased. The lack of an evaluation system for the efficiency of entrepreneurial practices at colleges and universities is also a factor in the low percentage of college students who are entrepreneurial. As a result, colleges and universities have an urgent challenge in boosting the effectiveness of college students’ entrepreneurial practices [6–10].When it comes to comprehending the utilitarian nature of college students’ entrepreneurship, colleges and universities have had a difficult time for a while. Due to the lack of recognition of college students’ entrepreneurship, colleges and universities fail to effectively use the role that college students’ entrepreneurial activities play in the development of their pupils. If universities are going to remain relevant in the current entrepreneurial and innovation-driven environment, it is essential to emphasize the importance of boosting the quality of college student’s entrepreneurship. We must support and promote college students’ entrepreneurial efforts, and we must increase the effectiveness of students’ entrepreneurial approaches to respond to the development trend in the social market. Colleges and universities will be able to better serve their communities and the nation as a whole, as well as better understand and fulfill their own inherent value and purpose. While this is true, the existing research on the entrepreneurial qualities of college students is still in a general theoretical discussion, necessitating more extensive, and in-depth theoretical investigation [11–15].In this setting, determining the efficacy of college students’ entrepreneurial methods has become a critical question. This work creates a neural network for analyzing the efficiency of college students’ entrepreneurial methods using neural network and computer data simulation technology. The systematic computer simulation verifies the effectiveness of the proposed method.The section-by-section study is given as follows: Section2 discussed the related work. Section 3 discussed the method of the proposed work. Section 4 discusses the experiments and results. Finally, the conclusion brings the paper to a finish in Section 5.


## 2. Related Work

Literature [16] believes that entrepreneurship refers to individuals grasping development opportunities, in order to realize their own value, to realize their own value by creating new products or service content. Literature [17] believes that, from an economic point of view, entrepreneurship means that individuals collect and use existing resources and capital to produce products or services that can create economic value, and at the same time obtain benefits and develop. Literature [18] believes that entrepreneurship is the establishment of an enterprise, which means that individuals plan the expected goals in a certain direction, design the production and manufacturing of the enterprise, and create a new product or service. Literature [19] believes that entrepreneurship can be discussed from two macrodirections. In a broad sense, entrepreneurship is the start of a new business, which is carried out by the starter of a business. Literature [20] believes that the fundamental essence of entrepreneurship is that under specific social conditions and environments, one or more essences have innovation, action, and courage. At the same time, entrepreneurs who can take risks form a team with other staff members, discover and gather resources, collect and capture resources, and use the opportunities contained in them to create new value activities. The enhanced PSO (KPSO) algorithm has a better global optimization ability, thus particles are less likely to fall into local extreme values. Second, maintain consistency between the particle dimension size in KPSO and the weights and thresholds in the BP network and create a mapping link between KPSO and the weights and thresholds. Literature [21] proposes the concept of entrepreneur, which is the essence of entrepreneurship. Risk is a part of entrepreneurship, and entrepreneurs must take certain risks in the process of starting a business. Literature [22] believes that entrepreneurial activities and opportunities are inseparable, and entrepreneurs must fully grasp market opportunities in order to make breakthroughs. Literature [23] believes that entrepreneurship is an individual’s creative thinking activities and specific behaviors, the combination of resources, and the establishment of economic organizations in the process of entrepreneurship. Entrepreneurs must have the opportunity and ability to grow in a risky environment. Entrepreneurship itself means establishing a new economic organization, and at the same time, in the process, which is significant to pay attention to uncontrollable factors as well as bear the risks that they may bring.Reference [24] proposed a three-element model, and successful entrepreneurial activities have the following three elements: entrepreneurial team, opportunities, and resources. The three resources must be constantly changed with the progress of the entire business activities to achieve a balance. Entrepreneurial activities start from opportunities. In the early stage of the business, the focus is to build a strong entrepreneurial team, followed by resource acquisition. Literature [25] believes that four elements of entrepreneur, environment, process, and organization influence and determine entrepreneurial performance. Entrepreneurship is to integrate different and independent behavioral elements through a reasonable structure to obtain the expected effect. This model can not only explain the creation behavior of a single enterprise but also explain the enterprise creation process and can guide the development of the enterprise. It is a dynamic development model. Literature [26] believes that the key elements of entrepreneurship are people, opportunities, the external environment, and its own transaction behavior, and the coordination of the four elements can promote the success of entrepreneurial activities. Literature [27] proposes that entrepreneurs should not only discover and take advantage of entrepreneurial opportunities but also form and lead organizations and manage organizational resources, and make adjustments and corrections in the process of continuous development of the organization, enhance the adaptability of the organization, and summarize the experience of the organization in success and failure. The dynamic learning process is the key to the success of entrepreneurship. Reference [28] pointed out that entrepreneurship can not only simply refer to the frequency of entrepreneurial behavior but also to the degree of influence of entrepreneurial activities. That is, whether the new enterprise represents high quality and is opportunity-driven, whether it creates higher value, and whether it has the possibility to optimize the regional economic situation. Reference [29] conducts research with entrepreneurial activities as the starting point and believes that high-quality entrepreneurship is characterized by opportunity-driven, innovative, and growth-oriented. In addition, they explored the relationship between entrepreneurial orientation, different economic resources, stable institutions, and high-quality entrepreneurship. Its findings suggest that for the above factors to fully reflect their role, the key lies in culture. Literature [30] creatively discusses the connotation of entrepreneurial quality from the regional level. It believes that the important basis for evaluating the quality of entrepreneurship is the expected profit level of the entrepreneur for the entrepreneurial opportunity, which he pointed out in the research. Under the premise of constant entrepreneurial opportunities in the region, entrepreneurs mainly consider the quality of opportunities. In the process of increasing new ventures, the quality of entrepreneurial opportunities that can be utilized is significantly reduced.


## 3. Method

This work first improves the PSO with the K-means algorithm to construct the KPSO algorithm. Then, the KPSO-BP network is constructed by combining the KPSO algorithm with the BP network to evaluate the effectiveness of the entrepreneurial methods of college students.


### 3.1. Particle Swarm Optimization

PSO is developed on the basis of abstracting and simulating the swarm model. The particle swarm searches for an optimal solution under the combined action of the global extremum and the individual extremum. The adjustment of the individual state is actually influenced by the combination of the optimal individual experience and the optimal group standard. The particle swarm optimization algorithm is the process of simulating the predation of flocks of birds. The particle swarm is composed of particles, and each particle contains attribute information, which is consistent with the number of parameters when solving a specific optimization problem. Each particle contains a position vector and a velocity vector. A position vector can be viewed as a set of solutions to an optimization problem.The process of PSO solving the optimization problem is to update the particle’s position vector and velocity vector through iteration:(1)vi,jt+1=wvi,jt+c1r1pbestt−xi,jt+c2r2gbestt−xi,jt,xi,jt+1=xi,jt+vi,jt.The right side of the velocity update formula has three pieces from a macroperspective. The first is the inertia part, which represents the original tendency of the particle. The second part is the self-awareness part, which contains the experience the particle itself has learned through previous iterations. The third part is social cognition, which represents the influence of the learning experience of all particles on that particle. The standard PSO is illustrated in Figure1.Figure 1
The pipeline of PSO.The particle swarm optimization algorithm does not use the genetic algorithm’s selection, crossover, or mutation procedures, instead relying on the optimum particles in the solution space to direct its search. It has a simple algorithm structure and runs at a high speed. A particle’s present ideal position will immediately attract the attention of other particles during the algorithm’s operation. The particle swarm will not be able to re-search the solution space if the optimal position is a locally optimal point. At this point in the algorithm’s development, a phenomenon known as premature convergence occurs.PSO is one of the typical representatives of intelligent optimization algorithms. This algorithm imitates the predation process of birds in nature, and it also belongs to the bionic algorithm. It is possible to develop an algorithm that is intelligent in its approach to optimization by using techniques like the genetic algorithm. The convergence, robustness, and efficiency of the PSO technique are all top-notch. Particle swarm optimization is more computationally efficient than the genetic approach. In addition, PSO does not need to calculate derivatives and estimates of feasible solutions, but iteratively solves according to the prediction function; therefore, it has a strong advantage in solving many discontinuous and multicoupling complex optimization problems.However, the traditional PSO has the disadvantages of being prone to premature convergence and falling into local optimum and difficult to deal with constraints. This paper hopes to use the K-means clustering algorithm to determine the cluster in each iteration process so that the particles can exchange information with the particles in the cluster without sacrificing too much time consumption. Obviously, the particles between the populations after the topological structure is determined are fixedly connected, and the particles can only communicate information to the particles connected to the topological structure. However, in the predation process of the actual flock of birds, it is easy to obtain information about a considerable number of individuals around. Therefore, this paper takes this as a starting point to establish a domain model that contains domain population information and is updated with the iteration of particle swarms.


### 3.2. K-Means Clustering

An algorithm for unsupervised learning the dynamic clustering method includesK-means. Using distance as a metric of similarity, dissimilar samples can be automatically categorized into a single group. As a starting point, K random objects are chosen to serve as clustering centers. This is followed by a recalculation based on the updated new class’s new average distance between each object and each seed cluster center. When the cluster centers do not change after two iterations, the clustering criterion has reached convergence, indicating that the data items have been properly adjusted. In multivariate statistical analysis, the K-means clustering algorithm falls under this category. Clustering algorithms such as K-means are an important part of data mining, and they have a number of advantages over other algorithms. The criterion function of the algorithm is(2)G=∑j=1K∑uxu−cj2.TheK-means clustering algorithm is simple in concept, but the accuracy with which the K value and the K initial cluster center locations are determined has a significant impact on the quality of the clustering effect. In the case of random selection, the best clustering results cannot be obtained in many cases. At present, some better selection methods for the initial cluster center points have been explored:(1)
SelectK points that are as far away from each other as possible.(2)
First, use the hierarchical clustering algorithm or Canopy algorithm to cluster the data, after obtainingK clusters, select a point from each cluster. It can be the center point of the cluster or the closest point to the center of the cluster.


### 3.3. Improved PSO withK-Means

Premature convergence is a common occurrence in heuristic optimization techniques. It is critical to improve population variety in order to avoid falling into the local optimum. Different from the proposals of various types of topological structures, this paper proposes a dynamic domainnbest model with a K-means clustering algorithm. The population’s topology is not set in this model; instead, after being classified by the clustering technique, the particles inside the cluster learn from one another nbest. Represents the optimal individual within the cluster.The traditional particle swarm optimization algorithm only uses the individual optimal value and the global optimal value, which actually wastes those particles that are currently second only to the optimal value but may represent other exploration direction information. An improved PSO is proposed. In each iteration, the particle swarm is divided into several subpopulations through theK-means clustering algorithm. The optimal particle in each cluster is then labeled as nbest. Each particle needs to learn not only from its own experience and the optimal particle of the population but also from the optimal particle in the clustering field:(3)vi,jt+1=wvi,jt+c1r1pbestt−xi,jt+c2r2gbestt−xi,jt+c3r3nbestt−xi,jt.In this paper, theK-means clustering algorithm is used to realize the clustering of each iteration. The time complexity of PSO is square, whereas the time complexity of K-means clustering is linear, due largely to the consideration of the algorithm’s time complexity. If the linear order is lower than the square order, it will not cause too much time sacrifice to the improved particle swarm optimization algorithm. The basic flow chart of KPSO is illustrated in Figure 2.Figure 2
The pipeline of KPSO.The specific steps of KPSO are as follows:(1)
Particle swarm initialization.(2)
Determine the clustering area according to the clustering algorithm.(3)
Calculate the optimal position searched for each particle so far according to the current position and velocity.(4)
According to the current position and velocity, the optimal position in each cluster area so far is calculated.(5)
Update velocity and position.(6)
PerformK-means clustering optimization for new individuals. Calculate and determine the new cluster center, and update the individual fitness value of the particles in each cluster area.(7)
If the current optimal particle does not meet the convergence conditions, the current particle swarm is reclustered. Repeat steps (2) to (6), if an empty cluster is produced, pick the particle that is farthest away from the current cluster center at random and assign it to this class. In all other cases, the global best solution is returned.


### 3.4. BP Network

BP network has been generally recognized by academia, and it has played a great role in various fields such as information, medicine, psychology, engineering, control, and transportation. Its learning method is primarily separated into the two stages below. Determine the input and output vectors first, which can reflect certain characteristics of the target parameters in general. Then determine the number of layers of the hidden layer and the number of neurons in each layer of the input layer, the hidden layer, and the output layer, that is, to build the topology of the network. In addition, each layer of the network’s thresholds and weights are clarified at the same time. Second, the calculation is carried out using the network framework described above, with the outcome of a comparison between the output squared error and the preset error. If the mistake is modest, the neuron is engaged, and the information is transmitted to the next layer’s matching neuron. If the error is significant, the parameters are reversed, i.e. the thresholds and weights between neurons in each layer are corrected repeatedly until the network’s learning process is complete.This process is consistent with the cognitive process law in the human learning process, that is, the human brain is simulated from the following three links:(1)
Use the neural network to acquire knowledge from the outside through the learning process.(2)
Internal neurons (i.e. thresholds and weights) store the acquired knowledge.(3)
The acquired knowledge is used for transfer to solve similar problems encountered next time. The above three links are repeated and interconnected, forming a complete and organic advanced intelligent system.BP neural network can realize any nonlinear mapping from input to output. BP neural network mainly includes three parts: an input layer, an output layer, and a hidden layer. Most networks have only one hidden layer, but with the deepening of theoretical research and the development of science and technology, a BP neural network model with multiple hidden layer structures has been formed. Threshold means that when the stimulus received by a neuron is greater than this threshold, this neuron will affect the stimulation of the next neuron. Then below this threshold, the stimulation of this neuron will not stimulate the next neuron. The hidden layer and output layer both have thresholds, whereas the input layer has none. Three shows the three-layer topology model of the BP neural network, as shown in Figure3.Figure 3
The pipeline of BP network.The powerful nonlinear mapping capability makes BP neural network outstanding in solving nonlinear problems, and strong self-learning, self-adaptive, and fault-tolerant capabilities are its unique advantages, and it is not surprising that it is widely used in many fields. But at the same time, limitations and deficiencies are still prominent.(1)
When creating a BP neural network, many parameters need to be set, which increases the learning burden to a certain extent. In addition, because of the universal approximation theorem, the determination of the hidden layer is not controversial. However, as of now, there is no unified and perfect mathematical formula to solve the problem of the number of neurons in the hidden layer. There are only some empirical formulas for reference. In practice, only a few more times can be set to compare the results. However, the number of hidden layer nodes is very important. If the network is too small, the anti-interference ability of the network will be poor, and if the network is too large, the training time will be too long.(2)
It is easy to fall into the local optimum. From a mathematical point of view, the traditional BP neural network is a local search optimization method. To solve a complex nonlinear problem, the weights of the network are gradually adjusted in the direction of local improvement. This will cause the algorithm to fall into a local extremum, and the weights will converge to a local minimum, resulting in network training failure.(3)
The convergence speed is slow, and the calculation efficiency is low.


### 3.5. Improved BP Network with KPSO

The improved particle swarm optimization algorithm can better avoid the trap of local extreme value when optimizing the weights and thresholds of the neural network. The main content of this chapter is to establish the BP network model (KPSO-BP) based on the improved particle swarm optimization algorithm and carry out the corresponding computer data simulation to analyze the effectiveness of the entrepreneurial methods of college students.The BP neural network serves as the foundation for the analysis model employed in this paper, and the particle swarm optimization algorithm is primarily used to optimize the weights and thresholds of the neural network. Therefore, the main prediction process is the training framework of the BP neural network, as shown in Figure4.Figure 4
The pipeline of KPSO-BP.The specific implementation steps of KPSO-BP are the following:(1)
Initialize the parameters of KPSO algorithm and BP network.(2)
Calculate the fitness function of the particle swarm and start the iteration. If the current fitness function value is smaller than the previous fitness function value, the current value is used as the individual optimal value; otherwise, it remains unchanged. If the current global optimal value is greater than the individual optimal value of the particle swarm, the individual optimal value at this time is the global optimal value of the particle swarm.(3)
The position vector and the velocity vector are updated by the formula of the KPSO algorithm as the weights and thresholds of the BP neural network for training and end when the number of training iterations is reached or the accuracy error is met; otherwise, go to the second step to continue.(4)
Output the results, conduct network tests, and analyze the results.


## 3.1. Particle Swarm Optimization

PSO is developed on the basis of abstracting and simulating the swarm model. The particle swarm searches for an optimal solution under the combined action of the global extremum and the individual extremum. The adjustment of the individual state is actually influenced by the combination of the optimal individual experience and the optimal group standard. The particle swarm optimization algorithm is the process of simulating the predation of flocks of birds. The particle swarm is composed of particles, and each particle contains attribute information, which is consistent with the number of parameters when solving a specific optimization problem. Each particle contains a position vector and a velocity vector. A position vector can be viewed as a set of solutions to an optimization problem.The process of PSO solving the optimization problem is to update the particle’s position vector and velocity vector through iteration:(1)vi,jt+1=wvi,jt+c1r1pbestt−xi,jt+c2r2gbestt−xi,jt,xi,jt+1=xi,jt+vi,jt.The right side of the velocity update formula has three pieces from a macroperspective. The first is the inertia part, which represents the original tendency of the particle. The second part is the self-awareness part, which contains the experience the particle itself has learned through previous iterations. The third part is social cognition, which represents the influence of the learning experience of all particles on that particle. The standard PSO is illustrated in Figure1.Figure 1
The pipeline of PSO.The particle swarm optimization algorithm does not use the genetic algorithm’s selection, crossover, or mutation procedures, instead relying on the optimum particles in the solution space to direct its search. It has a simple algorithm structure and runs at a high speed. A particle’s present ideal position will immediately attract the attention of other particles during the algorithm’s operation. The particle swarm will not be able to re-search the solution space if the optimal position is a locally optimal point. At this point in the algorithm’s development, a phenomenon known as premature convergence occurs.PSO is one of the typical representatives of intelligent optimization algorithms. This algorithm imitates the predation process of birds in nature, and it also belongs to the bionic algorithm. It is possible to develop an algorithm that is intelligent in its approach to optimization by using techniques like the genetic algorithm. The convergence, robustness, and efficiency of the PSO technique are all top-notch. Particle swarm optimization is more computationally efficient than the genetic approach. In addition, PSO does not need to calculate derivatives and estimates of feasible solutions, but iteratively solves according to the prediction function; therefore, it has a strong advantage in solving many discontinuous and multicoupling complex optimization problems.However, the traditional PSO has the disadvantages of being prone to premature convergence and falling into local optimum and difficult to deal with constraints. This paper hopes to use the K-means clustering algorithm to determine the cluster in each iteration process so that the particles can exchange information with the particles in the cluster without sacrificing too much time consumption. Obviously, the particles between the populations after the topological structure is determined are fixedly connected, and the particles can only communicate information to the particles connected to the topological structure. However, in the predation process of the actual flock of birds, it is easy to obtain information about a considerable number of individuals around. Therefore, this paper takes this as a starting point to establish a domain model that contains domain population information and is updated with the iteration of particle swarms.


## 3.2. K-Means Clustering

An algorithm for unsupervised learning the dynamic clustering method includesK-means. Using distance as a metric of similarity, dissimilar samples can be automatically categorized into a single group. As a starting point, K random objects are chosen to serve as clustering centers. This is followed by a recalculation based on the updated new class’s new average distance between each object and each seed cluster center. When the cluster centers do not change after two iterations, the clustering criterion has reached convergence, indicating that the data items have been properly adjusted. In multivariate statistical analysis, the K-means clustering algorithm falls under this category. Clustering algorithms such as K-means are an important part of data mining, and they have a number of advantages over other algorithms. The criterion function of the algorithm is(2)G=∑j=1K∑uxu−cj2.TheK-means clustering algorithm is simple in concept, but the accuracy with which the K value and the K initial cluster center locations are determined has a significant impact on the quality of the clustering effect. In the case of random selection, the best clustering results cannot be obtained in many cases. At present, some better selection methods for the initial cluster center points have been explored:(1)
SelectK points that are as far away from each other as possible.(2)
First, use the hierarchical clustering algorithm or Canopy algorithm to cluster the data, after obtainingK clusters, select a point from each cluster. It can be the center point of the cluster or the closest point to the center of the cluster.


## 3.3. Improved PSO withK-Means

Premature convergence is a common occurrence in heuristic optimization techniques. It is critical to improve population variety in order to avoid falling into the local optimum. Different from the proposals of various types of topological structures, this paper proposes a dynamic domainnbest model with a K-means clustering algorithm. The population’s topology is not set in this model; instead, after being classified by the clustering technique, the particles inside the cluster learn from one another nbest. Represents the optimal individual within the cluster.The traditional particle swarm optimization algorithm only uses the individual optimal value and the global optimal value, which actually wastes those particles that are currently second only to the optimal value but may represent other exploration direction information. An improved PSO is proposed. In each iteration, the particle swarm is divided into several subpopulations through theK-means clustering algorithm. The optimal particle in each cluster is then labeled as nbest. Each particle needs to learn not only from its own experience and the optimal particle of the population but also from the optimal particle in the clustering field:(3)vi,jt+1=wvi,jt+c1r1pbestt−xi,jt+c2r2gbestt−xi,jt+c3r3nbestt−xi,jt.In this paper, theK-means clustering algorithm is used to realize the clustering of each iteration. The time complexity of PSO is square, whereas the time complexity of K-means clustering is linear, due largely to the consideration of the algorithm’s time complexity. If the linear order is lower than the square order, it will not cause too much time sacrifice to the improved particle swarm optimization algorithm. The basic flow chart of KPSO is illustrated in Figure 2.Figure 2
The pipeline of KPSO.The specific steps of KPSO are as follows:(1)
Particle swarm initialization.(2)
Determine the clustering area according to the clustering algorithm.(3)
Calculate the optimal position searched for each particle so far according to the current position and velocity.(4)
According to the current position and velocity, the optimal position in each cluster area so far is calculated.(5)
Update velocity and position.(6)
PerformK-means clustering optimization for new individuals. Calculate and determine the new cluster center, and update the individual fitness value of the particles in each cluster area.(7)
If the current optimal particle does not meet the convergence conditions, the current particle swarm is reclustered. Repeat steps (2) to (6), if an empty cluster is produced, pick the particle that is farthest away from the current cluster center at random and assign it to this class. In all other cases, the global best solution is returned.


## 3.4. BP Network

BP network has been generally recognized by academia, and it has played a great role in various fields such as information, medicine, psychology, engineering, control, and transportation. Its learning method is primarily separated into the two stages below. Determine the input and output vectors first, which can reflect certain characteristics of the target parameters in general. Then determine the number of layers of the hidden layer and the number of neurons in each layer of the input layer, the hidden layer, and the output layer, that is, to build the topology of the network. In addition, each layer of the network’s thresholds and weights are clarified at the same time. Second, the calculation is carried out using the network framework described above, with the outcome of a comparison between the output squared error and the preset error. If the mistake is modest, the neuron is engaged, and the information is transmitted to the next layer’s matching neuron. If the error is significant, the parameters are reversed, i.e. the thresholds and weights between neurons in each layer are corrected repeatedly until the network’s learning process is complete.This process is consistent with the cognitive process law in the human learning process, that is, the human brain is simulated from the following three links:(1)
Use the neural network to acquire knowledge from the outside through the learning process.(2)
Internal neurons (i.e. thresholds and weights) store the acquired knowledge.(3)
The acquired knowledge is used for transfer to solve similar problems encountered next time. The above three links are repeated and interconnected, forming a complete and organic advanced intelligent system.BP neural network can realize any nonlinear mapping from input to output. BP neural network mainly includes three parts: an input layer, an output layer, and a hidden layer. Most networks have only one hidden layer, but with the deepening of theoretical research and the development of science and technology, a BP neural network model with multiple hidden layer structures has been formed. Threshold means that when the stimulus received by a neuron is greater than this threshold, this neuron will affect the stimulation of the next neuron. Then below this threshold, the stimulation of this neuron will not stimulate the next neuron. The hidden layer and output layer both have thresholds, whereas the input layer has none. Three shows the three-layer topology model of the BP neural network, as shown in Figure3.Figure 3
The pipeline of BP network.The powerful nonlinear mapping capability makes BP neural network outstanding in solving nonlinear problems, and strong self-learning, self-adaptive, and fault-tolerant capabilities are its unique advantages, and it is not surprising that it is widely used in many fields. But at the same time, limitations and deficiencies are still prominent.(1)
When creating a BP neural network, many parameters need to be set, which increases the learning burden to a certain extent. In addition, because of the universal approximation theorem, the determination of the hidden layer is not controversial. However, as of now, there is no unified and perfect mathematical formula to solve the problem of the number of neurons in the hidden layer. There are only some empirical formulas for reference. In practice, only a few more times can be set to compare the results. However, the number of hidden layer nodes is very important. If the network is too small, the anti-interference ability of the network will be poor, and if the network is too large, the training time will be too long.(2)
It is easy to fall into the local optimum. From a mathematical point of view, the traditional BP neural network is a local search optimization method. To solve a complex nonlinear problem, the weights of the network are gradually adjusted in the direction of local improvement. This will cause the algorithm to fall into a local extremum, and the weights will converge to a local minimum, resulting in network training failure.(3)
The convergence speed is slow, and the calculation efficiency is low.


## 3.5. Improved BP Network with KPSO

The improved particle swarm optimization algorithm can better avoid the trap of local extreme value when optimizing the weights and thresholds of the neural network. The main content of this chapter is to establish the BP network model (KPSO-BP) based on the improved particle swarm optimization algorithm and carry out the corresponding computer data simulation to analyze the effectiveness of the entrepreneurial methods of college students.The BP neural network serves as the foundation for the analysis model employed in this paper, and the particle swarm optimization algorithm is primarily used to optimize the weights and thresholds of the neural network. Therefore, the main prediction process is the training framework of the BP neural network, as shown in Figure4.Figure 4
The pipeline of KPSO-BP.The specific implementation steps of KPSO-BP are the following:(1)
Initialize the parameters of KPSO algorithm and BP network.(2)
Calculate the fitness function of the particle swarm and start the iteration. If the current fitness function value is smaller than the previous fitness function value, the current value is used as the individual optimal value; otherwise, it remains unchanged. If the current global optimal value is greater than the individual optimal value of the particle swarm, the individual optimal value at this time is the global optimal value of the particle swarm.(3)
The position vector and the velocity vector are updated by the formula of the KPSO algorithm as the weights and thresholds of the BP neural network for training and end when the number of training iterations is reached or the accuracy error is met; otherwise, go to the second step to continue.(4)
Output the results, conduct network tests, and analyze the results.


## 4. Experiments and Discussions

### 4.1. Dataset

This work collects data for the production of the experimental data set. The data set contains a total of 39,042 samples, of which 25,830 are training samples and the remaining 13,212 samples are the test set. The input data of each sample is the effectiveness index of college students’ entrepreneurial methods, as shown in Table1. Each sample is labeled with four different levels of entrepreneurial approach effectiveness. This work uses precision and recalls as evaluation metrics.Table 1
The effectiveness index of entrepreneurial methods.
IndexItemx1Sales growth ratex2Employee growth ratex3Member turnover ratex4Market share growthx5Possibility of going concernx6Service identificationx7Invention patent applicationsx8Proportion of R&D personnelx9Technology development fundsx10Return on investmentx11Entrepreneur satisfactionx12Employee satisfactionx13Number of jobs createdx14Average annual taxx15Customer satisfaction


### 4.2. Evaluation of Training Loss

In neural networks, the training of the network is very important. In order to verify that the network designed in this work can effectively converge on the training set, this paper conducts corresponding experiments to analyze the loss during the training process. The experimental results are illustrated in Figure5.Figure 5
The training loss.As training progresses, the network loss gradually decreases. When training epoch = 30, the loss value remains basically stable, which indicates that the network has reached a convergence state at this time. This experiment verifies the correctness of the KPSO-BP network structure.


### 4.3. Comparison with Other Methods

To verify the correctness of the KPSO-BP method proposed in this work for evaluating the effectiveness of college students’ entrepreneurial methods, it is compared with other methods. The compared methods include LR, SVM, and RBF, and the experimental results are illustrated in Table2.Table 2
Comparison with other methods.
MethodPrecisionRecallLR87.7085.60SVM91.6088.50RBF93.9092.90KPSO-BP95.7094.50The method designed in this work can achieve 95.7% precision and 94.5% recall. Compared with the best-performing RBF method in the table, the KPSO-BP method can achieve a precision improvement of 1.8% and a recall improvement of 1.6%. This verifies the effectiveness of the proposed method.


### 4.4. Evaluation of Evolutionary Algorithm

As mentioned earlier, this work uses the KPSO evolutionary algorithm to optimize the BP network. To verify the effectiveness of this strategy, this work conducts comparative experiments to compare the network performance without KPSO and when KPSO is used. The experimental results are illustrated in Figure6.Figure 6
Evaluation of evolutionary algorithm.Obviously, compared with not using KPSO algorithm, after using KPSO, it can obtain 2.4% precision and 1.6% recall improvement. Therefore, it can verify the validity and correctness of the KPSO method design in this work.


### 4.5. Evaluation ofK-Means

As mentioned earlier, this work utilizesK-means to optimize the PSO algorithm. To verify the effectiveness of this strategy, this work conducts corresponding comparative experiments to compare the network performance without K-means optimization and with K-means optimization. The experimental results are illustrated in Figure 7.Figure 7
Evaluation ofK-means.Obviously, compared with not usingK-means algorithm, after using K-means, it can obtain 1.2% precision and 1.0% recall improvement. Therefore, it can verify the validity and correctness of the K-means method design in this work.


## 4.1. Dataset

This work collects data for the production of the experimental data set. The data set contains a total of 39,042 samples, of which 25,830 are training samples and the remaining 13,212 samples are the test set. The input data of each sample is the effectiveness index of college students’ entrepreneurial methods, as shown in Table1. Each sample is labeled with four different levels of entrepreneurial approach effectiveness. This work uses precision and recalls as evaluation metrics.Table 1
The effectiveness index of entrepreneurial methods.
IndexItemx1Sales growth ratex2Employee growth ratex3Member turnover ratex4Market share growthx5Possibility of going concernx6Service identificationx7Invention patent applicationsx8Proportion of R&D personnelx9Technology development fundsx10Return on investmentx11Entrepreneur satisfactionx12Employee satisfactionx13Number of jobs createdx14Average annual taxx15Customer satisfaction


## 4.2. Evaluation of Training Loss

In neural networks, the training of the network is very important. In order to verify that the network designed in this work can effectively converge on the training set, this paper conducts corresponding experiments to analyze the loss during the training process. The experimental results are illustrated in Figure5.Figure 5
The training loss.As training progresses, the network loss gradually decreases. When training epoch = 30, the loss value remains basically stable, which indicates that the network has reached a convergence state at this time. This experiment verifies the correctness of the KPSO-BP network structure.


## 4.3. Comparison with Other Methods

To verify the correctness of the KPSO-BP method proposed in this work for evaluating the effectiveness of college students’ entrepreneurial methods, it is compared with other methods. The compared methods include LR, SVM, and RBF, and the experimental results are illustrated in Table2.Table 2
Comparison with other methods.
MethodPrecisionRecallLR87.7085.60SVM91.6088.50RBF93.9092.90KPSO-BP95.7094.50The method designed in this work can achieve 95.7% precision and 94.5% recall. Compared with the best-performing RBF method in the table, the KPSO-BP method can achieve a precision improvement of 1.8% and a recall improvement of 1.6%. This verifies the effectiveness of the proposed method.


## 4.4. Evaluation of Evolutionary Algorithm

As mentioned earlier, this work uses the KPSO evolutionary algorithm to optimize the BP network. To verify the effectiveness of this strategy, this work conducts comparative experiments to compare the network performance without KPSO and when KPSO is used. The experimental results are illustrated in Figure6.Figure 6
Evaluation of evolutionary algorithm.Obviously, compared with not using KPSO algorithm, after using KPSO, it can obtain 2.4% precision and 1.6% recall improvement. Therefore, it can verify the validity and correctness of the KPSO method design in this work.


## 4.5. Evaluation ofK-Means

As mentioned earlier, this work utilizesK-means to optimize the PSO algorithm. To verify the effectiveness of this strategy, this work conducts corresponding comparative experiments to compare the network performance without K-means optimization and with K-means optimization. The experimental results are illustrated in Figure 7.Figure 7
Evaluation ofK-means.Obviously, compared with not usingK-means algorithm, after using K-means, it can obtain 1.2% precision and 1.0% recall improvement. Therefore, it can verify the validity and correctness of the K-means method design in this work.


## 5. Conclusion

Colleges and universities are responsible for cultivating innovative people, and it is also a critical role for inventive countries to strengthen their international competitiveness. College students’ entrepreneurial activities are the focus of widespread social concern and should be an important task in colleges and universities. There is a lack of research on the effectiveness of college students’ entrepreneurial methods in the academic world. At present, most scholars discuss college students’ entrepreneurial education, entrepreneurial policies, and entrepreneurial management. Some scholars have also studied the ways to promote the quality of college students’ entrepreneurship from the aspects of personal characteristics, competencies, and entrepreneurship education in colleges and universities. Therefore, there is still a gap in the research on the effectiveness of college students’ entrepreneurial methods. This work relies on neural network and computer data simulation to evaluate the effectiveness of college students’ entrepreneurial methods. The contents of this work are the following:(1)
A dynamic domain population model based on the idea ofK-means is presented to address the phenomena of premature convergence of the classic particle swarm optimization algorithm when dealing with complex high-dimensional optimization problems. The model is dedicated to improving population diversity and avoiding falling into local extrema when searching for optimality. At each iteration, the K-means clustering algorithm is used to communicate information in the particle field.(2)
Use the powerful global search ability of the improved PSO algorithm to determine the parameters of the BP network. It primarily consists of the algorithm’s threshold and weight, which eliminates the local optimal solution and replaces it with a global search. Then, use the neural network trained above to evaluate and analyze the effectiveness of college students’ entrepreneurial methods.



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*Source: 6269827-2022-05-12.xml*