# Analysis of Key Factors Affecting Undergraduate Entrepreneurship Ability from a Big Data Perspective

Authors: Xiangmin Meng; Wenjun Huang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

To achieve rapid social and economic growth, society has raised expectations for the overall quality and competence of today’s college students in the environment of big data. College students must have a strong spirit of creation and entrepreneurship, as well as a precise level of professional knowledge and practical talents. Based on this backdrop, a weighted K-means clustering method (WK-means) is utilized to investigate the key determinants of college students’ entrepreneurial ability and their interactions with important text content, such as the college student innovation and entrepreneurship competition. To begin, extract a number of characteristics from the sample of entrepreneurial college students, particularly the entrepreneurial ability components. The sample is then divided into two groups. Entrepreneurs who succeed are categorized as positive, while those who fail are classified as negative. The clustering result yields the feature weight of each sample. The weight of each attribute is proportionate to its importance. Second, using the multivariate relationship analysis approach, the correlation between various factors is determined, and the impact of various factors on college students’ entrepreneurial capacity is further investigated. The results of the experiment demonstrate that adaptability and learning ability are the most important characteristics influencing college students’ entrepreneurial aptitude, whereas self-control has little impact.


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## Body

## 1. Introduction

The world has entered the era of big data. In 2014, “big data” was cited for the first time in a government work report. In 2015, the government suggested a national big data plan, which is already being executed. In 2018, the government pushed for paying attention to big data development, reviewing the current state and development trends, and developing large plans and big ideas ahead of time. As a result of the promotion of national big data policy and the development of big data technology, traditional business models are shifting, and entrepreneurial opportunities in big data-related industries are expanding. As a new force for social evolution, college students have an innate interest in and advantage over the Internet. Big data has ushered in a plethora of new research and job opportunities for college students. Only a small number of Chinese college students are currently involved in entrepreneurship, and the success rate is significantly lower. One of the major issues is a dearth of entrepreneurship skills among college students. This highlights the crucial need for colleges and universities to develop and modernize entrepreneurial talent training. Researchers must explore the underlying elements impacting college students’ entrepreneurial talents, as well as the interplay between those factors and the students’ academic situations and the external environment, in order to improve their entrepreneurial abilities.Many academics have studied the entrepreneurial abilities of college students from various perspectives. Each study looked at the components of entrepreneurial ability from a different angle. UNESCO specifies the meaning of a college student’s entrepreneurial capacity in terms of characteristics. From a wide and narrow perspective, Reference [1] provides primary and secondary indicators of entrepreneurial potential. From the perspective of resource integration, Reference [2] lists 14 characteristics of entrepreneurial competence. From the perspective of comprehensive ability, References [3, 4] split entrepreneurial ability into characteristics such as knowledge, ability, and literacy. According to Reference [5], college students’ entrepreneurial ability is classified into three dimensions: knowledge, skills, and attitude. Entrepreneurial capabilities are defined by Reference [6] in five dimensions: identifying available opportunities, driving the company to complete the entire process from creation to receipt, conceptual capabilities, policy capabilities, and the ability to apply tools and technologies in specific fields. Entrepreneurial capabilities are also divided into eight dimensions by Babson Business School in the United States: innovation and creativity, opportunity, ability to form an entrepreneurial team, marketing ability, entrepreneurial financing ability, leadership, ability to manage growing companies, and business ability. Scholar Man looked examined the six elements of entrepreneurial aptitude: opportunity ability, relationship ability, conceptual ability, strategy ability, and commitment ability. However, no instances have been provided to verify the statistical importance of these talents. The preceding study has identified a number of elements that influence entrepreneurial ability on various levels and performed a simple analysis of these aspects. Using machine learning algorithms [7, 8] to intelligently examine the aforesaid aspects and provide theoretical support for the improvement of entrepreneurial aptitude, this research builds on the previous work. This paper’s key contribution is as follows:
(1)
A machine learning method is utilized to intelligently examine the relevant aspects that affect entrepreneurial capacity, assisting in increasing the relevance and timeliness of government and school-based entrepreneurial education(2)
The WK-means clustering technique was proposed and used to analyze the entrepreneurial ability factors of college students. The algorithm assigns a weight to each element depending on the relevance of each factor’s impact on entrepreneurship, allowing for a more in-depth examination of each factor’s impact on entrepreneurship. The correlation between numerous elements can also be derived via multivariate correlation analysis, allowing for the examination of the global ideal combination of components(3)
Examine the association between various entrepreneurial skill components using the WK-means model’s running results. Some recommendations are made for the government and schools to foster the entrepreneurial capacity of college students based on the findings of the study


## 2. Related Work

### 2.1. Problems Faced by College Students in Starting a Business

Only a small number of Chinese college students are currently involved in entrepreneurship, and the success rate is significantly lower. One of the major issues is a dearth of entrepreneurship skills among college students. This highlights the crucial need for colleges and universities to develop and modernize entrepreneurial talent training. Researchers must explore the underlying elements impacting college students’ entrepreneurial talents, as well as the interplay between those factors and the students’ academic situations and the external environment, in order to improve their entrepreneurial abilities. Effective entrepreneurship is difficult to achieve due to the limitations of certain college students’ own expertise and competency, as well as a lack of practical experience, which eventually leads to entrepreneurial failure. The second problem is a lack of feasible company concepts. A well-designed entrepreneurial project can excite students’ interest in entrepreneurship while simultaneously providing them with the opportunity to develop more entrepreneurial skills and knowledge through project implementation, resulting in a successful firm. However, due to the constrained circumstances of college students, it is difficult to identify these projects in a timely manner, which creates certain barriers to college students’ successful entrepreneurship. The lack of appropriate entrepreneurial collaboration structures is the third issue. The current status of entrepreneurial cooperation is largely short-term and loose. At the same time, entrepreneurs primarily aim to use this model to bring appropriate entrepreneurial resources to themselves in order to better achieve their business. However, the partners’ interests will eventually be able to come to an agreement, which will make it impossible to maintain collaboration.Based on the aforementioned analysis of problems, this essay explores the potential for college students to establish a business under the new circumstances. For starters, a wider range of entrepreneurial prospects expands college students’ entrepreneurial options. College students may confront numerous barriers to entrepreneurship due to limits in skill, knowledge, and experience. College students, on the other hand, can still take advantage of numerous entrepreneurial tools, widen entrepreneurial paths and platforms, and realize their entrepreneurial aspirations as a result of the increase in entrepreneurial prospects. Second, it can help students better integrate theory and practice, adapt to changing social conditions, and continue to gain entrepreneurial experience.


### 2.2. Analysis on the Factors of Undergraduates’ Entrepreneurship Ability

To determine the factors that influence entrepreneurial potential, extensive research is conducted. Big data technology is used to collect, analyze, and process data about college students’ entrepreneurial talents. Scholars use many variables to determine a range of entrepreneurial factors. The analysis results of some regularly utilized entrepreneurial factors are listed below. It is determined to perform a comprehensive examination from many dimensions such as basic qualities, professional capabilities, and general capabilities from the perspective of resource integration. Finally, physical fitness, psychological quality, self-awareness, creative ability, leadership ability, risk management capacity, and other qualities of a college student’s entrepreneurial ability are ruled out as important indicators for determining their entrepreneurial potential. The results of the resource integration analysis of entrepreneurial factors are shown in Table1. Tables 2 and 3 also provide the results of the various angles of entrepreneurial analysis.Table 1
Typical example 1.
No.Detail1Learning ability2Opportunity ability3Knowledge application ability4Social skills5Communication skills6Self-control7Ability to fight failure8Adaptability9Creativity10Leadership11Planning ability12Management ability13Resource integration ability14Opportunity grasping abilityTable 2
Typical example 2.
First level indicatorSecondary indicatorsIndex descriptionBasic entrepreneurship abilityCreativityTeacher evaluation scoreProfessional knowledgeWeighted average score of professional coursesLegal knowledgeSelf-evaluation scoreSelf-learning abilityClass performance scorePractical abilityEvaluation score of the internship unitBasic qualities of entrepreneurshipToughnessPsychological assessment scoreOptimismPsychological assessment scoreConfidencePsychological assessment scoreResponsiblePsychological assessment scoreResiliencePsychological assessment scoreOpportunityOpportunity creation abilityIntern company evaluation scoreOpportunity recognition abilityIntern company evaluation scoreOpportunity utilizationIntern company evaluation scoreInformation gathering abilityScore based on Internet search informationManagement abilityTeam leadershipCounselor evaluation scoreFund management abilityMaterialScore for monthly living expenses managementManagement capabilitiesTechnicalPersonal belongings management scoreManagement abilityScore on the mastery of the selected relevant professional technologyMarketing abilityEvaluation score of the internship unitCommitment capacityRelatives and friends evaluation scoreChoice of entrepreneurial projectsScore of selected entrepreneurial project categoryRisk toleranceRisk identification and decision-making abilityRisk identification score in selected entrepreneurial projectsRisk control abilityRisk aversion score in selected entrepreneurial projectsTable 3
Typical example 3.
VariableCategoryMeasurement standardMeaningAssignmentIndependent variablePersonalGenderGender0: female, 1: maleProfessionalMajor0: liberal arts, 1: scienceEntrepreneurship practiceDo you have practical experience in entrepreneurship0: no, 1: yesFamilyRelatives and friendsEntrepreneurship with friends and relatives0: no, 1: yesFamily entrepreneurship funding supportWhether family entrepreneurship is supported by funds0: no, 1: yesSchoolSchool levelSchool level0: higher vocational, 1: general undergraduate, 2: key undergraduateEntrepreneurship educationHave you participated in entrepreneurship education courses0: no, 1: yesSocietyBusiness environmentCity business environment1: poor, 2: fair, 3: developedEntrepreneurship policyDo you understand entrepreneurial policies1: do not understand, 2: understandDependent variableEntrepreneurial abilityEntrepreneurshipEntrepreneurship0: low, 1: high


### 2.3. Analysis Model of Influencing Factors of Entrepreneurship Ability

Under the condition of resource synergy, the demands of entrepreneurial development are also showing a diversified trend. Many issues are not directly related to college students, but some unrelated factors are organically combined with college students’ entrepreneurship through resource coordination and resource integration. The development factor model of college students’ entrepreneurial potential is created using the resource synergy theory, as illustrated in Figure1. The entrepreneurial ability of college students mainly includes general ability, opportunity ability, and professional ability. General skills mainly include self-control, learning, communication, and adaptability. According to the resource synergy theory, the different factors of college students’ entrepreneurship can be organically combined.Figure 1
Analysis model of factors affecting entrepreneurial ability.


## 2.1. Problems Faced by College Students in Starting a Business

Only a small number of Chinese college students are currently involved in entrepreneurship, and the success rate is significantly lower. One of the major issues is a dearth of entrepreneurship skills among college students. This highlights the crucial need for colleges and universities to develop and modernize entrepreneurial talent training. Researchers must explore the underlying elements impacting college students’ entrepreneurial talents, as well as the interplay between those factors and the students’ academic situations and the external environment, in order to improve their entrepreneurial abilities. Effective entrepreneurship is difficult to achieve due to the limitations of certain college students’ own expertise and competency, as well as a lack of practical experience, which eventually leads to entrepreneurial failure. The second problem is a lack of feasible company concepts. A well-designed entrepreneurial project can excite students’ interest in entrepreneurship while simultaneously providing them with the opportunity to develop more entrepreneurial skills and knowledge through project implementation, resulting in a successful firm. However, due to the constrained circumstances of college students, it is difficult to identify these projects in a timely manner, which creates certain barriers to college students’ successful entrepreneurship. The lack of appropriate entrepreneurial collaboration structures is the third issue. The current status of entrepreneurial cooperation is largely short-term and loose. At the same time, entrepreneurs primarily aim to use this model to bring appropriate entrepreneurial resources to themselves in order to better achieve their business. However, the partners’ interests will eventually be able to come to an agreement, which will make it impossible to maintain collaboration.Based on the aforementioned analysis of problems, this essay explores the potential for college students to establish a business under the new circumstances. For starters, a wider range of entrepreneurial prospects expands college students’ entrepreneurial options. College students may confront numerous barriers to entrepreneurship due to limits in skill, knowledge, and experience. College students, on the other hand, can still take advantage of numerous entrepreneurial tools, widen entrepreneurial paths and platforms, and realize their entrepreneurial aspirations as a result of the increase in entrepreneurial prospects. Second, it can help students better integrate theory and practice, adapt to changing social conditions, and continue to gain entrepreneurial experience.


## 2.2. Analysis on the Factors of Undergraduates’ Entrepreneurship Ability

To determine the factors that influence entrepreneurial potential, extensive research is conducted. Big data technology is used to collect, analyze, and process data about college students’ entrepreneurial talents. Scholars use many variables to determine a range of entrepreneurial factors. The analysis results of some regularly utilized entrepreneurial factors are listed below. It is determined to perform a comprehensive examination from many dimensions such as basic qualities, professional capabilities, and general capabilities from the perspective of resource integration. Finally, physical fitness, psychological quality, self-awareness, creative ability, leadership ability, risk management capacity, and other qualities of a college student’s entrepreneurial ability are ruled out as important indicators for determining their entrepreneurial potential. The results of the resource integration analysis of entrepreneurial factors are shown in Table1. Tables 2 and 3 also provide the results of the various angles of entrepreneurial analysis.Table 1
Typical example 1.
No.Detail1Learning ability2Opportunity ability3Knowledge application ability4Social skills5Communication skills6Self-control7Ability to fight failure8Adaptability9Creativity10Leadership11Planning ability12Management ability13Resource integration ability14Opportunity grasping abilityTable 2
Typical example 2.
First level indicatorSecondary indicatorsIndex descriptionBasic entrepreneurship abilityCreativityTeacher evaluation scoreProfessional knowledgeWeighted average score of professional coursesLegal knowledgeSelf-evaluation scoreSelf-learning abilityClass performance scorePractical abilityEvaluation score of the internship unitBasic qualities of entrepreneurshipToughnessPsychological assessment scoreOptimismPsychological assessment scoreConfidencePsychological assessment scoreResponsiblePsychological assessment scoreResiliencePsychological assessment scoreOpportunityOpportunity creation abilityIntern company evaluation scoreOpportunity recognition abilityIntern company evaluation scoreOpportunity utilizationIntern company evaluation scoreInformation gathering abilityScore based on Internet search informationManagement abilityTeam leadershipCounselor evaluation scoreFund management abilityMaterialScore for monthly living expenses managementManagement capabilitiesTechnicalPersonal belongings management scoreManagement abilityScore on the mastery of the selected relevant professional technologyMarketing abilityEvaluation score of the internship unitCommitment capacityRelatives and friends evaluation scoreChoice of entrepreneurial projectsScore of selected entrepreneurial project categoryRisk toleranceRisk identification and decision-making abilityRisk identification score in selected entrepreneurial projectsRisk control abilityRisk aversion score in selected entrepreneurial projectsTable 3
Typical example 3.
VariableCategoryMeasurement standardMeaningAssignmentIndependent variablePersonalGenderGender0: female, 1: maleProfessionalMajor0: liberal arts, 1: scienceEntrepreneurship practiceDo you have practical experience in entrepreneurship0: no, 1: yesFamilyRelatives and friendsEntrepreneurship with friends and relatives0: no, 1: yesFamily entrepreneurship funding supportWhether family entrepreneurship is supported by funds0: no, 1: yesSchoolSchool levelSchool level0: higher vocational, 1: general undergraduate, 2: key undergraduateEntrepreneurship educationHave you participated in entrepreneurship education courses0: no, 1: yesSocietyBusiness environmentCity business environment1: poor, 2: fair, 3: developedEntrepreneurship policyDo you understand entrepreneurial policies1: do not understand, 2: understandDependent variableEntrepreneurial abilityEntrepreneurshipEntrepreneurship0: low, 1: high


## 2.3. Analysis Model of Influencing Factors of Entrepreneurship Ability

Under the condition of resource synergy, the demands of entrepreneurial development are also showing a diversified trend. Many issues are not directly related to college students, but some unrelated factors are organically combined with college students’ entrepreneurship through resource coordination and resource integration. The development factor model of college students’ entrepreneurial potential is created using the resource synergy theory, as illustrated in Figure1. The entrepreneurial ability of college students mainly includes general ability, opportunity ability, and professional ability. General skills mainly include self-control, learning, communication, and adaptability. According to the resource synergy theory, the different factors of college students’ entrepreneurship can be organically combined.Figure 1
Analysis model of factors affecting entrepreneurial ability.


## 3. Analysis on the Key Factors of Undergraduates’ Entrepreneurship

### 3.1. The Core Idea of Entrepreneurial Factor Analysis

To improve a college student’s entrepreneurial ability, it is critical to grasp the important factors that influence the development of their entrepreneurial ability. Key public-recognized factors, such as college students’ learning capacity, knowledge application ability, strategic ability, leadership ability, and other indicators, have been gained through the analysis findings of connected study. There are a variety of indicators, and each one has a different impact on the growth of entrepreneurial ability; therefore, it is important to weigh each one’s importance. Because the dimensions of the indicators change and there are interactions between indicators of different dimensions, this study recommends utilizing a feature-weighted WK-means method to perform cluster analysis on each indicator and output the weights of each feature. Figure2 depicts the fundamental concept.Figure 2
Core ideas.The current entrepreneurial ability indicators are fed into the WK-means model as feature data, which outputs the clustering findings and the weight of each feature, as illustrated in the diagram. Different samples will be divided into two categories: positive and negative. The positive category will reflect entrepreneurial success, while the negative category will represent entrepreneurial failure. Adjust the weight of each sample feature in real time until the best clustering result is reached. The best clustering result will determine the final weight of each feature. The weight of each characteristic is used to calculate the importance of distinct indicators in each category. This importance level also represents the amount to which different characteristics influence entrepreneurial ability. Based on the analysis results, a link between the features is determined in order to make relevant recommendations for improving college students’ entrepreneurial ability and assisting the government and universities in fostering college students’ entrepreneurial ability.


### 3.2. WK-Means Model

Cluster analysis is widely employed in a variety of industries, including data mining, image recognition, and others. It is a critical machine learning algorithm. The clustering algorithm [9, 10] can automatically divide the data objects into different categories, and the data in each category have similar features. Through the clustering algorithm, the inner law of the data can be dug out from the vast data. Common clustering algorithms can be divided into division method [11, 12], hierarchical method [13, 14], density method [15, 16], and grid method [17, 18], where the division method is the most widely used. The classic algorithms in the division method include K-means algorithm [19, 20], fuzzy C-means (FCM) [21], and possibility clustering (PCM) [22]. The K-means algorithm is a partition-based hard clustering algorithm. It has a quick computing speed and is particularly well suited to clustering high-dimensional, large-scale, and text data. It is a well-known clustering algorithm. K-means works by selecting K cluster centers at random, calculating the similarity between each sample and the cluster centers using Euclidean distance, and assigning the samples to the closest class. The traditional K-means algorithm has two problems. One is that the clustering result is unstable. The second is to calculate the sample similarity based on Euclidean distance. All features participate in the calculation of Euclidean distance and have the same contribution degree, which often has random fluctuations of varying sizes. Aiming at the first problem, References [23, 24] propose an improved K-means algorithm. The enhancement technique is to select the initial clustering center using probability, which considerably reduces the uncertainty of the classic K-means algorithm for randomly selecting the clustering center, thereby boosting clustering accuracy. The upgraded K-means method, on the other hand, only addresses the optimization of cluster centers and ignores the various contributions of features. References [25, 26] presented weighted K-means algorithms with different techniques to improve the clustering impact of the model for the second question. The WK-means method is used in this study to improve two elements of classic K-means. One is the optimization of the clustering’s initial center point, which is picked as far away from the sample as possible as the first clustering center. The second step is to develop a method for calculating feature weight and to improve the Euclidean distance computation formula.Set sample setX=xi∣xi∈X,i=1,2,⋯,n, each sample has m features xi=xi1,xi2,⋯,xim.


#### 3.2.1. Initial Cluster Center Optimization

The optimization strategy of clustering centers is as follows: choose a sample at random to serve as the clustering center, starting from the second category, calculate the minimum distanceminDistx between each data object x and the selected clustering center. Select the sample j where the maximum value is located in minDistx as the next cluster center. Each cluster center is determined so that the distance between it and the existing cluster center is greater, allowing the selected cluster center to be closer to the data distribution. Experiments suggest that remaining as far away from the cluster center as possible can improve the clustering algorithm’s accuracy. The following are the specific steps:Step 1. Choose a sample at random to serve as the first cluster centerV1, where k′ is the number of classes, k′=1.Step 2. Based on the Euclidean distance, calculate the distanceDistX,V between the sample set X and the existing class center.Step 3. Select the minimum distanceminDistX=minDistX,Vkk=1,2,⋯,k′ from the existing class in DistX,V as the distance value between the sample and the current class Vk.Step 4. Find the samplej to which the maximum value belongs as the next cluster center Vk in minDistx, k′=k′+1, Vk′=argmaxminDistX.Step 5. Repeat Step 2 untilk cluster centers are selected.


#### 3.2.2. Feature Weighting Strategy

Feature weight mainly measures the difference in importance of each feature. If a feature has little change, it means that the difference of the feature is small, and it further shows that its contribution to the classification is small, and it will be given a low weight. If a feature has a large change, it means that the feature is significant and further shows that its contribution to the classification is large, and it will be given a high weight.Define the feature difference degreep=p1,p2,⋯,pm to represent the feature change of each column, which is a one-dimensional vector. The degree of feature difference generally uses variance to represent the overall error of the feature. Because the value ranges of each feature are different, even if some features have large differences, if the value is small, the variance will be small. This paper uses a new formula for calculating the difference degree to measure the feature difference. Let
(1)pi=maxxi−minxiavgxi,wherexi represents the data of a certain column; maxxi indicates the column’s greatest value; minxi represents the column’s minimum value; and avgxi represents the column’s average value. The maximum-minimum difference represents the greatest degree of difference between the values in the column, and its ratio to the average value can be used to comprehend the overall difference. The greater the p value, the greater the degree of feature difference, and the greater the change in the feature data.Define the maximum ratiomaxr=maxpi/pj of the feature difference degree. pi and pj, respectively, represent the feature difference degree of the ith column and the jth column.Define the feature weightw=w1,w2,⋯,wm to represent the different contributions of m features calculated based on Euclidean distance. Use the logistic regression function Softmax to calculate w, the specific formula is
(2)wi=epi∑i=1mepi,wherepi is the degree of difference of each feature. The smaller the pi is, the smaller the contribution is; on the contrary, the greater the contribution.The Softmax function can highlight the maximum value and suppress other components below the maximum value. But the Softmax function will have the maximum or minimum feature weight. If a dataset has 3 feature weights0.056,0.036,0.908, and the third feature value is much larger than the first two, the feature weight calculation will have maximum or minimum. Due to the different value ranges of the eigenvalues, the weight value is completely out of balance. The Sigmoid function has strong robustness. Each feature weight value can be mapped to the (0,1) interval to balance the difference between feature weights, so Equation (2) can also be designed with the Sigmoid function as follows:
(3)wi=1+e−pi∑i=1m1+e−pi−1.After each feature is weighted, Euclidean distance and class error square sum SSE are also changed to weighted Euclidean distance and weighted SSE(S).(4)Distxi,xj=∑k=1mwkxik−xjk2,S=∑i=1n∑j=1kwxi−vj2.


### 3.3. Algorithm Steps

The Algorithm1 are the steps in the WK-means algorithm’s execution:Algorithm 1
Input: the number of clusters in the sample dataset X is K.Output: centers of clustering and feature weights1) According to the steps given in the initial cluster center optimization, selectk cluster centers Vi2) Calculate the feature difference degreepi of the dataset according to Equation (1)3) Calculate the maximum feature ratiomaxr. When maxr>10, it indicates that there is a problem of maximum and minimum feature weights; then Equation (3) is selected to calculate feature weights wi; when maxr<10, then Equation (2) is selected to calculate feature weights wi4) Using Equation (1), calculate the similarity between the sample and the central point (4), take the smallest similarity as the sample attribution category, and assign the sample to the category Li5) According to the samples divided byLi, calculate the average value of each feature of similar samples and update the cluster center Vi6) Calculate the variables in Equation (4) and repeat Step 2-Step 5 until the SSE remains unchanged or reaches the specified number of iterations.


## 3.1. The Core Idea of Entrepreneurial Factor Analysis

To improve a college student’s entrepreneurial ability, it is critical to grasp the important factors that influence the development of their entrepreneurial ability. Key public-recognized factors, such as college students’ learning capacity, knowledge application ability, strategic ability, leadership ability, and other indicators, have been gained through the analysis findings of connected study. There are a variety of indicators, and each one has a different impact on the growth of entrepreneurial ability; therefore, it is important to weigh each one’s importance. Because the dimensions of the indicators change and there are interactions between indicators of different dimensions, this study recommends utilizing a feature-weighted WK-means method to perform cluster analysis on each indicator and output the weights of each feature. Figure2 depicts the fundamental concept.Figure 2
Core ideas.The current entrepreneurial ability indicators are fed into the WK-means model as feature data, which outputs the clustering findings and the weight of each feature, as illustrated in the diagram. Different samples will be divided into two categories: positive and negative. The positive category will reflect entrepreneurial success, while the negative category will represent entrepreneurial failure. Adjust the weight of each sample feature in real time until the best clustering result is reached. The best clustering result will determine the final weight of each feature. The weight of each characteristic is used to calculate the importance of distinct indicators in each category. This importance level also represents the amount to which different characteristics influence entrepreneurial ability. Based on the analysis results, a link between the features is determined in order to make relevant recommendations for improving college students’ entrepreneurial ability and assisting the government and universities in fostering college students’ entrepreneurial ability.


## 3.2. WK-Means Model

Cluster analysis is widely employed in a variety of industries, including data mining, image recognition, and others. It is a critical machine learning algorithm. The clustering algorithm [9, 10] can automatically divide the data objects into different categories, and the data in each category have similar features. Through the clustering algorithm, the inner law of the data can be dug out from the vast data. Common clustering algorithms can be divided into division method [11, 12], hierarchical method [13, 14], density method [15, 16], and grid method [17, 18], where the division method is the most widely used. The classic algorithms in the division method include K-means algorithm [19, 20], fuzzy C-means (FCM) [21], and possibility clustering (PCM) [22]. The K-means algorithm is a partition-based hard clustering algorithm. It has a quick computing speed and is particularly well suited to clustering high-dimensional, large-scale, and text data. It is a well-known clustering algorithm. K-means works by selecting K cluster centers at random, calculating the similarity between each sample and the cluster centers using Euclidean distance, and assigning the samples to the closest class. The traditional K-means algorithm has two problems. One is that the clustering result is unstable. The second is to calculate the sample similarity based on Euclidean distance. All features participate in the calculation of Euclidean distance and have the same contribution degree, which often has random fluctuations of varying sizes. Aiming at the first problem, References [23, 24] propose an improved K-means algorithm. The enhancement technique is to select the initial clustering center using probability, which considerably reduces the uncertainty of the classic K-means algorithm for randomly selecting the clustering center, thereby boosting clustering accuracy. The upgraded K-means method, on the other hand, only addresses the optimization of cluster centers and ignores the various contributions of features. References [25, 26] presented weighted K-means algorithms with different techniques to improve the clustering impact of the model for the second question. The WK-means method is used in this study to improve two elements of classic K-means. One is the optimization of the clustering’s initial center point, which is picked as far away from the sample as possible as the first clustering center. The second step is to develop a method for calculating feature weight and to improve the Euclidean distance computation formula.Set sample setX=xi∣xi∈X,i=1,2,⋯,n, each sample has m features xi=xi1,xi2,⋯,xim.


### 3.2.1. Initial Cluster Center Optimization

The optimization strategy of clustering centers is as follows: choose a sample at random to serve as the clustering center, starting from the second category, calculate the minimum distanceminDistx between each data object x and the selected clustering center. Select the sample j where the maximum value is located in minDistx as the next cluster center. Each cluster center is determined so that the distance between it and the existing cluster center is greater, allowing the selected cluster center to be closer to the data distribution. Experiments suggest that remaining as far away from the cluster center as possible can improve the clustering algorithm’s accuracy. The following are the specific steps:Step 1. Choose a sample at random to serve as the first cluster centerV1, where k′ is the number of classes, k′=1.Step 2. Based on the Euclidean distance, calculate the distanceDistX,V between the sample set X and the existing class center.Step 3. Select the minimum distanceminDistX=minDistX,Vkk=1,2,⋯,k′ from the existing class in DistX,V as the distance value between the sample and the current class Vk.Step 4. Find the samplej to which the maximum value belongs as the next cluster center Vk in minDistx, k′=k′+1, Vk′=argmaxminDistX.Step 5. Repeat Step 2 untilk cluster centers are selected.


### 3.2.2. Feature Weighting Strategy

Feature weight mainly measures the difference in importance of each feature. If a feature has little change, it means that the difference of the feature is small, and it further shows that its contribution to the classification is small, and it will be given a low weight. If a feature has a large change, it means that the feature is significant and further shows that its contribution to the classification is large, and it will be given a high weight.Define the feature difference degreep=p1,p2,⋯,pm to represent the feature change of each column, which is a one-dimensional vector. The degree of feature difference generally uses variance to represent the overall error of the feature. Because the value ranges of each feature are different, even if some features have large differences, if the value is small, the variance will be small. This paper uses a new formula for calculating the difference degree to measure the feature difference. Let
(1)pi=maxxi−minxiavgxi,wherexi represents the data of a certain column; maxxi indicates the column’s greatest value; minxi represents the column’s minimum value; and avgxi represents the column’s average value. The maximum-minimum difference represents the greatest degree of difference between the values in the column, and its ratio to the average value can be used to comprehend the overall difference. The greater the p value, the greater the degree of feature difference, and the greater the change in the feature data.Define the maximum ratiomaxr=maxpi/pj of the feature difference degree. pi and pj, respectively, represent the feature difference degree of the ith column and the jth column.Define the feature weightw=w1,w2,⋯,wm to represent the different contributions of m features calculated based on Euclidean distance. Use the logistic regression function Softmax to calculate w, the specific formula is
(2)wi=epi∑i=1mepi,wherepi is the degree of difference of each feature. The smaller the pi is, the smaller the contribution is; on the contrary, the greater the contribution.The Softmax function can highlight the maximum value and suppress other components below the maximum value. But the Softmax function will have the maximum or minimum feature weight. If a dataset has 3 feature weights0.056,0.036,0.908, and the third feature value is much larger than the first two, the feature weight calculation will have maximum or minimum. Due to the different value ranges of the eigenvalues, the weight value is completely out of balance. The Sigmoid function has strong robustness. Each feature weight value can be mapped to the (0,1) interval to balance the difference between feature weights, so Equation (2) can also be designed with the Sigmoid function as follows:
(3)wi=1+e−pi∑i=1m1+e−pi−1.After each feature is weighted, Euclidean distance and class error square sum SSE are also changed to weighted Euclidean distance and weighted SSE(S).(4)Distxi,xj=∑k=1mwkxik−xjk2,S=∑i=1n∑j=1kwxi−vj2.


## 3.2.1. Initial Cluster Center Optimization

The optimization strategy of clustering centers is as follows: choose a sample at random to serve as the clustering center, starting from the second category, calculate the minimum distanceminDistx between each data object x and the selected clustering center. Select the sample j where the maximum value is located in minDistx as the next cluster center. Each cluster center is determined so that the distance between it and the existing cluster center is greater, allowing the selected cluster center to be closer to the data distribution. Experiments suggest that remaining as far away from the cluster center as possible can improve the clustering algorithm’s accuracy. The following are the specific steps:Step 1. Choose a sample at random to serve as the first cluster centerV1, where k′ is the number of classes, k′=1.Step 2. Based on the Euclidean distance, calculate the distanceDistX,V between the sample set X and the existing class center.Step 3. Select the minimum distanceminDistX=minDistX,Vkk=1,2,⋯,k′ from the existing class in DistX,V as the distance value between the sample and the current class Vk.Step 4. Find the samplej to which the maximum value belongs as the next cluster center Vk in minDistx, k′=k′+1, Vk′=argmaxminDistX.Step 5. Repeat Step 2 untilk cluster centers are selected.


## 3.2.2. Feature Weighting Strategy

Feature weight mainly measures the difference in importance of each feature. If a feature has little change, it means that the difference of the feature is small, and it further shows that its contribution to the classification is small, and it will be given a low weight. If a feature has a large change, it means that the feature is significant and further shows that its contribution to the classification is large, and it will be given a high weight.Define the feature difference degreep=p1,p2,⋯,pm to represent the feature change of each column, which is a one-dimensional vector. The degree of feature difference generally uses variance to represent the overall error of the feature. Because the value ranges of each feature are different, even if some features have large differences, if the value is small, the variance will be small. This paper uses a new formula for calculating the difference degree to measure the feature difference. Let
(1)pi=maxxi−minxiavgxi,wherexi represents the data of a certain column; maxxi indicates the column’s greatest value; minxi represents the column’s minimum value; and avgxi represents the column’s average value. The maximum-minimum difference represents the greatest degree of difference between the values in the column, and its ratio to the average value can be used to comprehend the overall difference. The greater the p value, the greater the degree of feature difference, and the greater the change in the feature data.Define the maximum ratiomaxr=maxpi/pj of the feature difference degree. pi and pj, respectively, represent the feature difference degree of the ith column and the jth column.Define the feature weightw=w1,w2,⋯,wm to represent the different contributions of m features calculated based on Euclidean distance. Use the logistic regression function Softmax to calculate w, the specific formula is
(2)wi=epi∑i=1mepi,wherepi is the degree of difference of each feature. The smaller the pi is, the smaller the contribution is; on the contrary, the greater the contribution.The Softmax function can highlight the maximum value and suppress other components below the maximum value. But the Softmax function will have the maximum or minimum feature weight. If a dataset has 3 feature weights0.056,0.036,0.908, and the third feature value is much larger than the first two, the feature weight calculation will have maximum or minimum. Due to the different value ranges of the eigenvalues, the weight value is completely out of balance. The Sigmoid function has strong robustness. Each feature weight value can be mapped to the (0,1) interval to balance the difference between feature weights, so Equation (2) can also be designed with the Sigmoid function as follows:
(3)wi=1+e−pi∑i=1m1+e−pi−1.After each feature is weighted, Euclidean distance and class error square sum SSE are also changed to weighted Euclidean distance and weighted SSE(S).(4)Distxi,xj=∑k=1mwkxik−xjk2,S=∑i=1n∑j=1kwxi−vj2.


## 3.3. Algorithm Steps

The Algorithm1 are the steps in the WK-means algorithm’s execution:Algorithm 1
Input: the number of clusters in the sample dataset X is K.Output: centers of clustering and feature weights1) According to the steps given in the initial cluster center optimization, selectk cluster centers Vi2) Calculate the feature difference degreepi of the dataset according to Equation (1)3) Calculate the maximum feature ratiomaxr. When maxr>10, it indicates that there is a problem of maximum and minimum feature weights; then Equation (3) is selected to calculate feature weights wi; when maxr<10, then Equation (2) is selected to calculate feature weights wi4) Using Equation (1), calculate the similarity between the sample and the central point (4), take the smallest similarity as the sample attribution category, and assign the sample to the category Li5) According to the samples divided byLi, calculate the average value of each feature of similar samples and update the cluster center Vi6) Calculate the variables in Equation (4) and repeat Step 2-Step 5 until the SSE remains unchanged or reaches the specified number of iterations.


## 4. Results and Analysis of the Experiments

### 4.1. Experimental Background

In this research, the analysis of entrepreneurial ability elements is to obtain positive and negative sample sets through a clustering algorithm. The positive category represents entrepreneurial success, and the negative category represents entrepreneurial failure. Through market research, a total of 100 entrepreneurial teams or individuals are collected. Among the 100 samples, 32 are positive samples and 68 samples are negative samples. The entrepreneurial ability factors adopt the 14 elements shown in Table1. The data of 14 entrepreneurial ability elements of 100 samples were extracted, respectively, and each element was scored by experts. Each element is scored on a scale of 1 to 5, with the following definitions: 1 indicates extreme weakness, 2 indicates moderate weakness, 3 indicates medium strength, 4 indicates strong strength, and 5 indicates extremely powerful strength. Different clustering algorithms are used to classify the sample set, and the clustering results output the weight of each feature at the same time. Each feature of the sample represents an element, so that each element and the impact of different elements on entrepreneurial ability can be obtained.Based on this idea, we choose K-means [27], FCM [28], and weighted FCM [29] as the comparison algorithm. Each algorithm’s parameter settings are identical to the reference. Normalized Mutual Information (NMI) [29] and Rand Index (RI) [29] are used as evaluation indicators. Table 4 gives the following descriptions of the two indicators:Table 4
Index description.
IndexExpressionRemarksNMI∑i=1C∑j=1CSijlogSSij/SiSj∑i=1CSilogSi/S∑j=1CSjlogSj/SSi is the data sample size included in the ith cluster, Sj is the data sample size included in the class j, and S is the overall size of the complete data sample.RIp00+p11SS−1/2The number of paired samples with different class labels and belonging to various classes ispoo, the number of paired samples with the same class label and belonging to the same class is p11, and the size of the complete sample set is S.The value ranges of the above two evaluation indicators are both [0,1]. The closer the value is to 1, the better the clustering performance of the algorithm. The experimental results obtained by each algorithm are the mean and variance obtained by running 20 times.


### 4.2. Results and Analysis of the Experiments

Table5 and Figure 3 demonstrate the clustering analysis findings for 100 samples of each comparison procedure.Table 5
Clustering results of each algorithm.
Index\algorithmK-meansFCMWFCMWK-meansNMI0.75330.78910.81200.8944NMI_std0.06420.08540.06920.0397RI0.78480.80820.82670.9008RI_std0.05320.06850.05540.0356Figure 3
Comparison of clustering results.The clustering impact of the two weighted algorithms, WFCM and WK-means, is much superior than the classic K-means and FCM algorithms, as shown in Table5. This shows that the strategy of weighting feature vectors can improve clustering performance. The WK-means algorithm used is better than the WFCM algorithm, which shows that the weighting strategy in this paper is more suitable for this application scenario. From the standard deviation data, it can be seen that the K-means algorithm is obviously more stable than the FCM algorithm. The algorithm used has further improved the stability of the algorithm due to the introduction of the weighting strategy.The clustering result based on the WK-means algorithm not only outputs the label of each sample but also obtains the weight of each feature of the sample. After the 14-dimensional data is preprocessed and feature extracted, 6 features are finally selected: 1: self-control, 2: learning ability, 3: adaptability, 4: leadership, 5: management ability, and 6: financial management ability. After clustering analysis of the WK-means algorithm, the weights of these 6 features are shown in Table6.Table 6
Feature weight.
No.123456Weights0.13280.19150.16290.12450.18630.2020The data in Table6 shows that among the six core factors of entrepreneurial ability, the most important are financial management ability, learning ability, and business ability. Second is the ability to respond, with self-control and leadership ranked last. In order to further study the relationship between each feature, this paper dynamically adjusts the weight of each feature to calculate the clustering results under different weight conditions. And analyze the mutual restriction between different features. The weight selection intervals of the 6 features are 0.1,0.3, and the change step is 0.005. From the results of multivariate correlation analysis, it can be seen that the learning ability and adaptability are highly correlated, which is a positive correlation. The correlation between operating ability and financial management ability is relatively large, which is a positive correlation.


## 4.1. Experimental Background

In this research, the analysis of entrepreneurial ability elements is to obtain positive and negative sample sets through a clustering algorithm. The positive category represents entrepreneurial success, and the negative category represents entrepreneurial failure. Through market research, a total of 100 entrepreneurial teams or individuals are collected. Among the 100 samples, 32 are positive samples and 68 samples are negative samples. The entrepreneurial ability factors adopt the 14 elements shown in Table1. The data of 14 entrepreneurial ability elements of 100 samples were extracted, respectively, and each element was scored by experts. Each element is scored on a scale of 1 to 5, with the following definitions: 1 indicates extreme weakness, 2 indicates moderate weakness, 3 indicates medium strength, 4 indicates strong strength, and 5 indicates extremely powerful strength. Different clustering algorithms are used to classify the sample set, and the clustering results output the weight of each feature at the same time. Each feature of the sample represents an element, so that each element and the impact of different elements on entrepreneurial ability can be obtained.Based on this idea, we choose K-means [27], FCM [28], and weighted FCM [29] as the comparison algorithm. Each algorithm’s parameter settings are identical to the reference. Normalized Mutual Information (NMI) [29] and Rand Index (RI) [29] are used as evaluation indicators. Table 4 gives the following descriptions of the two indicators:Table 4
Index description.
IndexExpressionRemarksNMI∑i=1C∑j=1CSijlogSSij/SiSj∑i=1CSilogSi/S∑j=1CSjlogSj/SSi is the data sample size included in the ith cluster, Sj is the data sample size included in the class j, and S is the overall size of the complete data sample.RIp00+p11SS−1/2The number of paired samples with different class labels and belonging to various classes ispoo, the number of paired samples with the same class label and belonging to the same class is p11, and the size of the complete sample set is S.The value ranges of the above two evaluation indicators are both [0,1]. The closer the value is to 1, the better the clustering performance of the algorithm. The experimental results obtained by each algorithm are the mean and variance obtained by running 20 times.


## 4.2. Results and Analysis of the Experiments

Table5 and Figure 3 demonstrate the clustering analysis findings for 100 samples of each comparison procedure.Table 5
Clustering results of each algorithm.
Index\algorithmK-meansFCMWFCMWK-meansNMI0.75330.78910.81200.8944NMI_std0.06420.08540.06920.0397RI0.78480.80820.82670.9008RI_std0.05320.06850.05540.0356Figure 3
Comparison of clustering results.The clustering impact of the two weighted algorithms, WFCM and WK-means, is much superior than the classic K-means and FCM algorithms, as shown in Table5. This shows that the strategy of weighting feature vectors can improve clustering performance. The WK-means algorithm used is better than the WFCM algorithm, which shows that the weighting strategy in this paper is more suitable for this application scenario. From the standard deviation data, it can be seen that the K-means algorithm is obviously more stable than the FCM algorithm. The algorithm used has further improved the stability of the algorithm due to the introduction of the weighting strategy.The clustering result based on the WK-means algorithm not only outputs the label of each sample but also obtains the weight of each feature of the sample. After the 14-dimensional data is preprocessed and feature extracted, 6 features are finally selected: 1: self-control, 2: learning ability, 3: adaptability, 4: leadership, 5: management ability, and 6: financial management ability. After clustering analysis of the WK-means algorithm, the weights of these 6 features are shown in Table6.Table 6
Feature weight.
No.123456Weights0.13280.19150.16290.12450.18630.2020The data in Table6 shows that among the six core factors of entrepreneurial ability, the most important are financial management ability, learning ability, and business ability. Second is the ability to respond, with self-control and leadership ranked last. In order to further study the relationship between each feature, this paper dynamically adjusts the weight of each feature to calculate the clustering results under different weight conditions. And analyze the mutual restriction between different features. The weight selection intervals of the 6 features are 0.1,0.3, and the change step is 0.005. From the results of multivariate correlation analysis, it can be seen that the learning ability and adaptability are highly correlated, which is a positive correlation. The correlation between operating ability and financial management ability is relatively large, which is a positive correlation.


## 5. Conclusion

This study looked into the factors that influence college students’ entrepreneurial potential through an in-depth examination of entrepreneurial literature and market data. The entrepreneurial ability components used in this study were generated through a comparison of the division of entrepreneurial ability factors among various college students. Machine learning algorithms are used to examine the structure of goals, criteria, and decision-making after defining the relevant variables of entrepreneurial ability and to comprehensively study the relationship between college students’ entrepreneurial ability and numerous elements. In this study, the cluster analysis algorithm from the machine learning algorithm is used to cluster the entrepreneurial sample set in order to find the best clustering model. The weight of each characteristic of the sample is decided based on the output result of the optimal clustering model. Each factor is considered a sample feature, and each feature is weighted. The feature’s weight indicates that it has a significant impact on the clustering outcome and that it will also have an impact on college students’ entrepreneurial abilities. The WK-means clustering technique is used in this study to output the weight of each feature, inferring the degree of influence of each aspect on entrepreneurial skill. This study used a multivariate correlation analysis method to further investigate the relationship between numerous parameters and discovered that learning capacity and adaptability are substantially correlated, with a positive association. The relationship between operating ability and financial management ability is rather strong, indicating a favorable relationship. However, there are several limitations to this research. Experts’ choices of entrepreneurial aptitude variables and their assessment of multiple entrepreneurial ability factors, for example, are subjective. Future work will also need to be optimized in this area.



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*Source: 2198948-2022-01-21.xml*