# Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

Authors: Lijun Bai; Chuanchao Wang; Lili Zhang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

At present, college students’ innovation and entrepreneurship has gradually become a hot topic nowadays, and the popularity of related knowledge of college students’ innovation and entrepreneurship is getting higher and higher, and more college students have joined in independent innovation and entrepreneurship. In order to alleviate a series of problems in college students’ innovation and entrepreneurship, we design an SD model under computational intelligence to put forward corresponding solutions to the problems faced in innovation and entrepreneurship, through the establishment of relevant models and experiments to verify that SD model is based on computational intelligence to optimize the design of college students’ innovation and entrepreneurship system. We know that the dimensions of tools include three dimensions: supply, demand, and environment. With the change of time, college students’ choice of tools for innovation and entrepreneurship has developed from a single supply tool to a multidimensional tool choice. Through the establishment of SD computational intelligence model to optimize the innovation and entrepreneurship system of college students, through the survey data, it can be seen that the prediction error value of SD model is about 9% and less than 10%, which is within the negligible error range, indicating that SD model has an optimization effect on the innovation and entrepreneurship system of college students. According to the survey, the three dimensions of capital investment, experience, and education level have an impact on college students’ innovation and entrepreneurship, accounting for 45%, 25%, and 30%, respectively. By comparing GW model, FIT model, and PBL model with SD model proposed in this paper, the data of precision, recall rate, accuracy rate, and F1 are compared and analyzed under the condition of changing the three dimensions of capital investment, experience, and education level, so as to select the best optimization model of innovation and entrepreneurship system for computational intelligence college students. Experiments show that when the capital investment increases by 10%, the relevant data of SD model is the best, with accuracy of 0.8761, recall rate of 0.9563, accuracy rate of 0.8972, and F1 of 0.8773; When the experience is increased by 10%, the optimization efficiency of SD model is the best, with its accuracy of 0.8660, recall rate of 0.9462, accuracy rate of 0.8871, and F1 of 0.8672. When the education level is increased by 10%, the optimization efficiency of SD model is the best, with accuracy of 0.8921, recall rate of 0.9762, accuracy rate of 0.8762, and F1 of 0.8861.


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## Body

## 1. Introduction

By comparing SD model with GW, FIT, and PBL model under the change of three dimensions affecting college students’ innovation and entrepreneurship, through relevant data and effective conclusions, it can be seen that SD model is more efficient than other models in optimizing the system, through the evolution of innovative and entrepreneurial tools for college students with the development of the times to make a better choice of tools and to enhance the popularity of college students’ innovation and entrepreneurship. In the future, the SD model proposed in this paper should be given priority when optimizing the innovation and entrepreneurship system of college students, so as to solve the difficulties faced by college students’ innovation and entrepreneurship.Literature [1] promotes the development of computer Go through the holding of computational intelligence contest. Experts have analyzed the four attributes of Go: weakness, time, game behavior, and main advantages, so as to further develop and study the field of computer Go in computational intelligence to improve the skills of computer Go. Literature [2] explores whether computer intelligence can be used to evaluate intelligence by studying the problems and existing relationships between intelligence and the real world. In literature [3], investigators study the rapid development of computational intelligence by preserving and collecting a large amount of professional knowledge. Researchers combine computational intelligence with computer technology to obtain the relevant information between automation and framework from a new height, so as to provide more help for the survey program and shorten the survey time. Literature [4] determines whether the data in each library is accurate and whether the process is executed normally by deadlock detection method in Java library. In order to verify this method, 18 libraries are verified, and the results show that 13 libraries are unlocked. This method can be used normally. Literature [5] discusses that the spirit of innovation and entrepreneurship in mature companies is the result of individualism and collectivism. Through the research of many companies, the results show that the innovation and entrepreneurship spirit is the highest in mature companies under the condition of balanced individual and collectivism. Literature [6] considers users’ cognition of color through computational intelligent aided design system. Customers’ perception of color plays a key role in determining whether they like an item or not. This paper overcomes the time-consuming problem of traditional color psychological questionnaire by color search method and grey system theory. Literature [7] forms a cooperative network by combining computational intelligence with online games, to investigate that the collaborative network reduced the degree of centralization observed new nodes and obtained a new exponential cooperative distribution. The popularity and topic degree of computational intelligence are improved. The advantages of the cooperative network are analyzed. Literature [8] connects natural intelligence with artificial intelligence through tie model. Literature [9] connects natural intelligence with artificial intelligence through tie model. In literature [10], in order to achieve the new goal of talent training in colleges and universities, we should strengthen the cultivation of college students’ innovative and entrepreneurial ability. In order to solve the educational problems in colleges and universities, literature [11] combines innovation and entrepreneurship with education and provides a better way for college students’ innovation and entrepreneurship development. Literature [12] cultivates students’ creativity and innovation through the educational concepts of creativity, innovation, and entrepreneurship. In literature [13], many colleges and universities have reformed their students’ education methods to adapt to the development of e-commerce, in order to improve e-commerce measures and talent training plan. In literature [14], innovation and entrepreneurship reflect the personality education of individual differences in the continuous development of the times. Literature [15], improves the demand for innovative and entrepreneurial talents of college students and promotes the innovation and entrepreneurship of college graduates.


## 2. Computational Intelligence and Design of College Students’ Innovation and Entrepreneurship System

### 2.1. Overview of Computational Intelligence

Computational intelligence includes neural network, evolutionary computation, and fuzzy computation, among which evolutionary computation includes Darwin theory, simulated society, and others [16]. Darwin’s theory includes four parts: genetic algorithm, evolutionary strategy, evolutionary rules, and genetic rules [17]. Simulated society includes ant colony algorithm and particle swarm optimization algorithm. Others are various manual algorithms, as shown in Figure 1.Figure 1
Overview of computational intelligence.The characteristics of computational intelligence are as follows: intelligence: self-adaptability to the algorithm, independent of the characteristics of the problem itself; parallelism: optimize the problem in the way of group cooperation; and robustness: the algorithm has good fault tolerance and can find the optimal solution under different conditions.


### 2.2. Design of Innovation and Entrepreneurship System for College Students

College students’ innovation and entrepreneurship are divided into microlevel and macrolevel [18]. The microlevel is the coordination and cooperation among alumni, entrepreneurial teams, and family cooperation; at the macrolevel, it is based on universities, guided by government departments, assisted by society, and supported by enterprises, as shown in Figure 2.Figure 2
Theoretical diagram.Macrolevel: university-based: entrepreneurship education and R&D transfer; government guidance: government policies and government entrepreneurial projects; social assistance: cultural norms and physical infrastructure; and enterprise support: technical cooperation and financial support.Through investigation, analysis, and design, college students’ ability is divided into four parts: innovation and entrepreneurship driving force, management ability, discovery ability, and creativity ability [19]. The driving forces of innovation and entrepreneurship include internal and external driving forces. Management ability is divided into operation, team organization, coordination, and operation ability [20]. In order to improve college students’ innovation and entrepreneurship ability, we should train college students from various aspects and reserve their innovation and entrepreneurship ability and knowledge base [21], as shown in Figure 3.Figure 3
Design drawing of college students’ innovation and entrepreneurship.


## 2.1. Overview of Computational Intelligence

Computational intelligence includes neural network, evolutionary computation, and fuzzy computation, among which evolutionary computation includes Darwin theory, simulated society, and others [16]. Darwin’s theory includes four parts: genetic algorithm, evolutionary strategy, evolutionary rules, and genetic rules [17]. Simulated society includes ant colony algorithm and particle swarm optimization algorithm. Others are various manual algorithms, as shown in Figure 1.Figure 1
Overview of computational intelligence.The characteristics of computational intelligence are as follows: intelligence: self-adaptability to the algorithm, independent of the characteristics of the problem itself; parallelism: optimize the problem in the way of group cooperation; and robustness: the algorithm has good fault tolerance and can find the optimal solution under different conditions.


## 2.2. Design of Innovation and Entrepreneurship System for College Students

College students’ innovation and entrepreneurship are divided into microlevel and macrolevel [18]. The microlevel is the coordination and cooperation among alumni, entrepreneurial teams, and family cooperation; at the macrolevel, it is based on universities, guided by government departments, assisted by society, and supported by enterprises, as shown in Figure 2.Figure 2
Theoretical diagram.Macrolevel: university-based: entrepreneurship education and R&D transfer; government guidance: government policies and government entrepreneurial projects; social assistance: cultural norms and physical infrastructure; and enterprise support: technical cooperation and financial support.Through investigation, analysis, and design, college students’ ability is divided into four parts: innovation and entrepreneurship driving force, management ability, discovery ability, and creativity ability [19]. The driving forces of innovation and entrepreneurship include internal and external driving forces. Management ability is divided into operation, team organization, coordination, and operation ability [20]. In order to improve college students’ innovation and entrepreneurship ability, we should train college students from various aspects and reserve their innovation and entrepreneurship ability and knowledge base [21], as shown in Figure 3.Figure 3
Design drawing of college students’ innovation and entrepreneurship.


## 3. Correlation Formula

### 3.1. Computational Intelligent Cang Lang Optimization Algorithm

Behavior simulation algorithm:Xt is the position vector, and n is the dimension of the position vector.The value of parameter decreases from 2 to 4 with the increase of iteration times.Xt is the current position vector, and Xt+1 is the updated position vector.
(1)D=C⋅Xprey−Xt,(2)Xt+1=Xprey−A⋅D,(3)A=2ar1−a,(4)C=2r2.Actual optimal value:T represents the current number of iterations
(5)Dα=C1⋅Xα−Xt,Dβ=C2⋅Xβ−Xt,Dδ=C3⋅Xδ−Xt,(6)X1t+1=Xα−A1⋅Dα,X2t+1=Xβ−A2⋅Dβ,X3t+1=Xδ−A3⋅Dδ,(7)Xt+1=X1t+1+X2t+1+X3t+13.Convergence performance analysis of the algorithm:R11, R12, R21, R22, R31, and R32 they are all random values in [0, 1] [22].
(8)Xt+1=13X1t+1+X2t+1+X3t+1=13Xα−A1⋅C1⋅Xα−Xt+Xβ−A2⋅C2⋅Xβ−Xt+Xδ−A3⋅C3⋅Xδ−Xt,(9)xijt+1=13xαj−2atr11−at2r12xαj−xijt+xβj−2atr21−at2r22xβj−xijt+xδj−2atr31−at2r32xδj−xijt=13xαj+xβj+xδj−13at2r11−12r12xαj−xijt+2r21−12r21−12r22xβj−xijt+2r31−12r32xδj−xijt,Whenlimt⟶Gmaxat=0 is
(10)limt⟶Gmaxxijt+1=13xαj+xβj+xδj.


### 3.2. MR-GWO Algorithm

Elimination and reorganization mechanism(11)xikt+1=xmin+r1×xmax−xmin.


### 3.3. Improvement of MR-GWO Algorithm

(12)ifxikt>xmax,xikt=xmax,ifxikt<xmin,xikt=xmin.


### 3.4. Memetic Algorithm

K is the number of communities in the network, and 1/t and 1/k are standardization factors.
(13)Rc=1t∑q=1t1k∑p=1ksp,q.N is the number of network nodes
(14)Rc1=1N∑q=1n1k∑p=1ksp,q,(15)Rc2=1k∑u=1ks2u.α is the weighting parameter, 0≤α<0.5 computational intelligence is attacked by large-scale targets, and 0.5<α≤1 is attacked by small-scale targets.
(16)Rc=αRc1+1−αRc2.Evaluation index of Memetic algorithm:(17)NMIA,B=−2∑i=1nA∑j=1nBFijlogFijN/Fi⋅Fj∑i=1nAFilogFi/N+∑j=1nBFjlogFj/N.The larger the NMI value, the more similar it is.(18)ΔEc=∑i=1k∑p,q∈siMpq′−Mpq/2.ΔEc<0 optimization has no obvious effect, ΔEc>0 optimization has obvious effect, and ΔEc=0 optimization fails [23].


### 3.5. Evaluation System of College Students’ Innovation and Entrepreneurship

Establishment of judgment matrix:(19)CkB1B2⋯BnB1b11b12⋯b1nB2b22b22⋯b2n⋯⋯⋯⋯b3nBnbn1bn2⋯bnn=Aijn×n,(20)AijW1W2W3W4W1W2W3W4=A111/311/5A23131/5A311/311/5A45551.Consistency check:(21)Wi=∏j=1naij1/n∑k=1n∏j=1naij1/ni=1,2,3,⋯,n.


### 3.6. Grey Relational Calculation of Innovation and Entrepreneurship

Contribution rate ofTi as main component
(22)Ti=λi/∑i=1nλi.Vector corresponding to principal componenti(23)M=M1,M2,⋯Mn.Index weight(24)bi=∑Ti∗Mi.P is the grey correlation resolution coefficient.
(25)Pij=bmin+p∗bmaxΔij+p∗bmax,(26)Qi=∑Wj×Pij.


## 3.1. Computational Intelligent Cang Lang Optimization Algorithm

Behavior simulation algorithm:Xt is the position vector, and n is the dimension of the position vector.The value of parameter decreases from 2 to 4 with the increase of iteration times.Xt is the current position vector, and Xt+1 is the updated position vector.
(1)D=C⋅Xprey−Xt,(2)Xt+1=Xprey−A⋅D,(3)A=2ar1−a,(4)C=2r2.Actual optimal value:T represents the current number of iterations
(5)Dα=C1⋅Xα−Xt,Dβ=C2⋅Xβ−Xt,Dδ=C3⋅Xδ−Xt,(6)X1t+1=Xα−A1⋅Dα,X2t+1=Xβ−A2⋅Dβ,X3t+1=Xδ−A3⋅Dδ,(7)Xt+1=X1t+1+X2t+1+X3t+13.Convergence performance analysis of the algorithm:R11, R12, R21, R22, R31, and R32 they are all random values in [0, 1] [22].
(8)Xt+1=13X1t+1+X2t+1+X3t+1=13Xα−A1⋅C1⋅Xα−Xt+Xβ−A2⋅C2⋅Xβ−Xt+Xδ−A3⋅C3⋅Xδ−Xt,(9)xijt+1=13xαj−2atr11−at2r12xαj−xijt+xβj−2atr21−at2r22xβj−xijt+xδj−2atr31−at2r32xδj−xijt=13xαj+xβj+xδj−13at2r11−12r12xαj−xijt+2r21−12r21−12r22xβj−xijt+2r31−12r32xδj−xijt,Whenlimt⟶Gmaxat=0 is
(10)limt⟶Gmaxxijt+1=13xαj+xβj+xδj.


## 3.2. MR-GWO Algorithm

Elimination and reorganization mechanism(11)xikt+1=xmin+r1×xmax−xmin.


## 3.3. Improvement of MR-GWO Algorithm

(12)ifxikt>xmax,xikt=xmax,ifxikt<xmin,xikt=xmin.


## 3.4. Memetic Algorithm

K is the number of communities in the network, and 1/t and 1/k are standardization factors.
(13)Rc=1t∑q=1t1k∑p=1ksp,q.N is the number of network nodes
(14)Rc1=1N∑q=1n1k∑p=1ksp,q,(15)Rc2=1k∑u=1ks2u.α is the weighting parameter, 0≤α<0.5 computational intelligence is attacked by large-scale targets, and 0.5<α≤1 is attacked by small-scale targets.
(16)Rc=αRc1+1−αRc2.Evaluation index of Memetic algorithm:(17)NMIA,B=−2∑i=1nA∑j=1nBFijlogFijN/Fi⋅Fj∑i=1nAFilogFi/N+∑j=1nBFjlogFj/N.The larger the NMI value, the more similar it is.(18)ΔEc=∑i=1k∑p,q∈siMpq′−Mpq/2.ΔEc<0 optimization has no obvious effect, ΔEc>0 optimization has obvious effect, and ΔEc=0 optimization fails [23].


## 3.5. Evaluation System of College Students’ Innovation and Entrepreneurship

Establishment of judgment matrix:(19)CkB1B2⋯BnB1b11b12⋯b1nB2b22b22⋯b2n⋯⋯⋯⋯b3nBnbn1bn2⋯bnn=Aijn×n,(20)AijW1W2W3W4W1W2W3W4=A111/311/5A23131/5A311/311/5A45551.Consistency check:(21)Wi=∏j=1naij1/n∑k=1n∏j=1naij1/ni=1,2,3,⋯,n.


## 3.6. Grey Relational Calculation of Innovation and Entrepreneurship

Contribution rate ofTi as main component
(22)Ti=λi/∑i=1nλi.Vector corresponding to principal componenti(23)M=M1,M2,⋯Mn.Index weight(24)bi=∑Ti∗Mi.P is the grey correlation resolution coefficient.
(25)Pij=bmin+p∗bmaxΔij+p∗bmax,(26)Qi=∑Wj×Pij.


## 4. Optimization of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

### 4.1. Related Policy Released

It can be seen from the release chart of related policy shown in Figure4 that 1998-2004 is the initial period of college students’ innovation and entrepreneurship. During this period, there are few related articles that were issued, two in 1998 and 2002; there were 3 articles in 2000 and 2004, which showed that college students had less awareness of college students’ innovation and entrepreneurship at that time, and the popularity of innovation and entrepreneurship was not comprehensive enough. From 2006 to 2012, it is the development period. Every year, there are more literatures on innovation and entrepreneurship than before, with 3 articles in 2006 and 4 articles in 2008-2012. It can be seen from the relevant data that in the development period, more college students have a deeper understanding of entrepreneurship [24]. In the following 2014-2020, college students’ innovation and entrepreneurship have entered an all-round development, and the number of documents published every year has increased significantly compared with the previous one, among which 6 related documents were published every year in 2016 and 2020. In the future, I believe more college students will join the innovation and entrepreneurship in order to realize their own value [25], as shown in the following figure.Figure 4
Release chart of college students’ innovation and entrepreneurship policy.


### 4.2. Dimension and Evolution Analysis of College Students’ Innovation and Entrepreneurship Tools

#### 4.2.1. Dimension of Innovation and Entrepreneurship Tools

Through Table1, the dimensions of the system tools are analyzed. The supply dimensions include training supply, capital investment, facility establishment, entrepreneurship support, and government procurement. Demand-based tools include management methods, legal types, and other aspects. Environmental type includes financial support. Through the investigation, we can find that there are many supply tools used in the initial stage, but after entering the development period, the choice of tools becomes wider, which shows that using different dimension tools in different periods can improve the efficiency of innovation and entrepreneurship. The dimensions of tools have increased, and the methods have also increased on a large scale. From 1998 to 2004, the proportion of innovative and entrepreneurial tools was 3.77%, from 2006 to 2012, 29.56%, and from 2014 to 2020, 66.67%. From the data, it can be seen that the proportion of innovative and entrepreneurial tools for college students is increasing year by year, indicating that modern tools provide convenience for college students’ double creation system, as shown in the following table.Table 1
Dimension table of college students’ innovation and entrepreneurship tools.
The dimension of college students’ innovation and entrepreneurship tools1998-20042006-20122014-2020Supply typeTraining supply31820Capital investment135Facility establishment014Entrepreneurship support158Government procurement003Outsourcing003Demand typeBusiness barrier036Entrepreneurial exchange037Legal management025Environmental typeFinancial support113Preferential tax0023Strategic measures01119Total647106Proportion3.77%29.56%66.67%As shown in Figure5, according to the time period of double creation system, it is divided into three time periods, namely, the initial period, the development period, and the all-round development period. In 1998-2004 and 2006-2012, supply tools used redundant demand tools and environmental tools, but after 2014-2020, college students’ innovation and entrepreneurship used more environmental tools than supply tools, which shows that with the development of the times, environmental tools play a positive role in college students’ innovation and entrepreneurship. Demand-based tools have been increasing year by year since 1998, and the growth is relatively stable, which shows that demand-based tools are indispensable for innovation and entrepreneurship, as shown in the following figure.Figure 5
Tool dimension diagram.


#### 4.2.2. Analysis on the Evolution of Innovation and Entrepreneurship Tools

As shown in Figure6, comparing the total effectiveness of tools with the average effectiveness of tools, it can be seen that from 1998 to 2008, the total effectiveness of tools was about 200, of which 220 was in 2006, and 450 was in 2010 and 2020, reaching the peak. With the increase of years, the effectiveness of tools also increases slowly. It can be seen from the average effectiveness of tools that it reached a peak of 1400 around 2010. With the increase of years, the use of tools has become an indispensable way for college students to innovate and start businesses, as shown in the following figure.Figure 6
Tool analysis diagram.


### 4.3. Test of SD Innovation and Entrepreneurship Model of Computational Intelligence

By comparing college students’ innovation and entrepreneurship, preparing entrepreneurs and starting entrepreneurs, this paper analyzes and tests the SD model of computational intelligence. It can be seen from Table2 that from 2012 to 2020, the number of college students who innovate and start businesses and prepare entrepreneurs and start businesses is increasing. Prepare entrepreneurs to be 563 in 2020; start-ups will reach the best of 189 in 2020. The maximum error of innovation and entrepreneurship for college students is 5.78%, the maximum error of preparing entrepreneurs is 9.19%, and the maximum error of starting entrepreneurs is 5.26%. From the experimental results, we can see that the SD model of computational intelligence has great benefits for the system optimization of double creation system, and the test results are normal, as shown in the following table.Table 2
SD model innovation and entrepreneurship test form.
YearCollege students’ innovation and entrepreneurshipPrepare entrepreneursEntrepreneurial starterActual valueAnalog valueError valueActual valueAnalog valueError valueActual valueAnalog valueError value20122702815.21%2572808.95%57605.26%20142802904.55%299290-3.01%1121174.46%20162852985.78%362351-3.03%130126-3.08%20182993042.09%479435-9.19%1671701.80%20203003093.75%5635691.66%1891911.06%According to the relevant data in Table2, the maximum error value is about 9%, lower than 10%, which belongs to the normal error range. Through the test of SD model, we can conclude that SD model has a good effect on the optimization of college students’ innovation and entrepreneurship system and can effectively guide double creation system.


#### 4.3.1. Simulation Results of SD Model under Benchmark Condition

As shown in Figure7, the simulation results of SD model under the benchmark situation show that since 2012, double creation system, preparation for entrepreneurship, and startup are on the rise. Therefore, SD model can be used to optimize the innovation and entrepreneurship system and design of college students under computational intelligence and create a better innovation and entrepreneurship system to promote more college students’ innovation and entrepreneurship, as shown in the following figure.Figure 7
SD renderings of reference conditions.The advantage lies in the fact that when the three dimensions of capital investment, experience, and education degree of innovation and entrepreneurship change accordingly, the SD model lies in the accuracy and recall rate, and the values on F1 are stable and high, so it has better optimization performance.


### 4.4. Comparison of Optimization Models of Innovation and Entrepreneurship of College Students with Computational Intelligence under Three Dimensions

This paper discusses the comparison of innovation and entrepreneurship models of computational intelligence college students from three dimensions: double creation system capital investment, double creation system experience, and double creation system education level. According to relevant survey data, the impact of these three dimensions on innovation and entrepreneurship is shown in the following figure, of which investment funds account for up to 45%. Without sufficient capital investment, no matter how good the innovation and entrepreneurship plan is, it cannot be implemented, followed by the level of education, accounting for 30%. Only with a good level of education can we have newer and more perfect ideas to support double creation system; finally, it is the sense of experience, accounting for 25%. Only by understanding the double creation system mechanism and experiencing the double creation system process can we better cope with the difficulties and risks faced in innovation and entrepreneurship, as shown in Figure8.Figure 8
Dimension map of innovation and entrepreneurship impact.


#### 4.4.1. Investment in Innovation and Venture Capital Increased by 10%

As shown in Table3 and Figure 9, after improving the capital investment dimension of innovation and entrepreneurship, GW model, FIT model, PBL model, and SD model proposed in this paper are compared in four aspects: Precision, recall rate, accuracy, and F1. As shown in the following table, when the capital investment is increased by 10%, the optimization performance of SD model is the best, with accuracy of 0.8761, recall rate of 0.9563, accuracy rate of 0.8972, and F1 of 0.8773. Secondly, FIT model has better optimization performance, while GW model has poor optimization effect.Table 3
Comparison of models under the change of capital investment.
ModelPrecisionRecall rateAccuracyF1SD model0.87610.95630.89720.8773GW model0.85270.93380.72310.8543FIT model0.86970.93250.87190.8763PBL model0.86870.93590.75430.8681Figure 9
Model comparison diagram.


#### 4.4.2. The Experience of Innovation and Entrepreneurship Has Increased by 10%

As shown in Table4 and Figure 10, when the experience of innovation and entrepreneurship increases by 10%, it can be seen from the following table combined with the following figure that the SD model proposed in this paper takes precedence over other models in terms of accuracy, recall rate, accuracy rate, and F1, followed by FIT model and GW model, and the optimization efficiency of PBL model is poor when the experience of innovation and entrepreneurship changes.Table 4
Comparison table of models under the change of experience.
ModelPrecisionRecall rateAccuracyF1SD model0.86600.94620.88710.8672GW model0.84270.91380.87010.8443FIT model0.84970.92950.87550.8463PBL model0.84870.92490.79430.8681Figure 10
Model comparison diagram.


#### 4.4.3. The Level of Double Creation Education Has Increased by 10%

As shown in Table5 and Figure 11, when the education level of innovation and entrepreneurship increases by 10%, the optimization effect of SD model is better than other models by combining data and images, and its accuracy, recall rate, accuracy rate, and F1 values are 0.8921, 0.9762, 0.8762, and 0.8861, respectively. Secondly, FIT model and PBL model, compared with GW model for college students’ innovation and entrepreneurship system optimization effect, is poor.Table 5
Comparison table of models under the change of teaching level.
ModelPrecisionRecall rateAccuracyF1SD model0.89210.97620.87620.8861GW model0.86520.88970.87340.8338FIT model0.87510.96550.83270.8553PBL model0.83470.96940.83210.8781Figure 11
Model comparison diagram.As we know from the above data and charts, the investment in innovation and entrepreneurship, experience, and education level all increase by 10%. The SD model proposed in this paper has a better optimization effect on double creation system. Therefore, SD double creation system optimization model with computational intelligence has more advantages than other models, which can improve efficiency and optimize the system.Accuracy is the quantity reflecting the proximity between the measured result and the true value; recall rate and precision rate are two measures widely used in the field of information retrieval and statistical classification, which are used to evaluate the quality of results. By comparing SD model with GW, FIT, and PBL model in precision, recall rate, accuracy, and F1 performance under three dimensions, it is known that when the three dimensions are changed, the precision, recall rate, accuracy, and F1 values of SD model are higher than other models, which shows that SD model has better performance for innovation and entrepreneurship system optimization.


## 4.1. Related Policy Released

It can be seen from the release chart of related policy shown in Figure4 that 1998-2004 is the initial period of college students’ innovation and entrepreneurship. During this period, there are few related articles that were issued, two in 1998 and 2002; there were 3 articles in 2000 and 2004, which showed that college students had less awareness of college students’ innovation and entrepreneurship at that time, and the popularity of innovation and entrepreneurship was not comprehensive enough. From 2006 to 2012, it is the development period. Every year, there are more literatures on innovation and entrepreneurship than before, with 3 articles in 2006 and 4 articles in 2008-2012. It can be seen from the relevant data that in the development period, more college students have a deeper understanding of entrepreneurship [24]. In the following 2014-2020, college students’ innovation and entrepreneurship have entered an all-round development, and the number of documents published every year has increased significantly compared with the previous one, among which 6 related documents were published every year in 2016 and 2020. In the future, I believe more college students will join the innovation and entrepreneurship in order to realize their own value [25], as shown in the following figure.Figure 4
Release chart of college students’ innovation and entrepreneurship policy.


## 4.2. Dimension and Evolution Analysis of College Students’ Innovation and Entrepreneurship Tools

### 4.2.1. Dimension of Innovation and Entrepreneurship Tools

Through Table1, the dimensions of the system tools are analyzed. The supply dimensions include training supply, capital investment, facility establishment, entrepreneurship support, and government procurement. Demand-based tools include management methods, legal types, and other aspects. Environmental type includes financial support. Through the investigation, we can find that there are many supply tools used in the initial stage, but after entering the development period, the choice of tools becomes wider, which shows that using different dimension tools in different periods can improve the efficiency of innovation and entrepreneurship. The dimensions of tools have increased, and the methods have also increased on a large scale. From 1998 to 2004, the proportion of innovative and entrepreneurial tools was 3.77%, from 2006 to 2012, 29.56%, and from 2014 to 2020, 66.67%. From the data, it can be seen that the proportion of innovative and entrepreneurial tools for college students is increasing year by year, indicating that modern tools provide convenience for college students’ double creation system, as shown in the following table.Table 1
Dimension table of college students’ innovation and entrepreneurship tools.
The dimension of college students’ innovation and entrepreneurship tools1998-20042006-20122014-2020Supply typeTraining supply31820Capital investment135Facility establishment014Entrepreneurship support158Government procurement003Outsourcing003Demand typeBusiness barrier036Entrepreneurial exchange037Legal management025Environmental typeFinancial support113Preferential tax0023Strategic measures01119Total647106Proportion3.77%29.56%66.67%As shown in Figure5, according to the time period of double creation system, it is divided into three time periods, namely, the initial period, the development period, and the all-round development period. In 1998-2004 and 2006-2012, supply tools used redundant demand tools and environmental tools, but after 2014-2020, college students’ innovation and entrepreneurship used more environmental tools than supply tools, which shows that with the development of the times, environmental tools play a positive role in college students’ innovation and entrepreneurship. Demand-based tools have been increasing year by year since 1998, and the growth is relatively stable, which shows that demand-based tools are indispensable for innovation and entrepreneurship, as shown in the following figure.Figure 5
Tool dimension diagram.


### 4.2.2. Analysis on the Evolution of Innovation and Entrepreneurship Tools

As shown in Figure6, comparing the total effectiveness of tools with the average effectiveness of tools, it can be seen that from 1998 to 2008, the total effectiveness of tools was about 200, of which 220 was in 2006, and 450 was in 2010 and 2020, reaching the peak. With the increase of years, the effectiveness of tools also increases slowly. It can be seen from the average effectiveness of tools that it reached a peak of 1400 around 2010. With the increase of years, the use of tools has become an indispensable way for college students to innovate and start businesses, as shown in the following figure.Figure 6
Tool analysis diagram.


## 4.2.1. Dimension of Innovation and Entrepreneurship Tools

Through Table1, the dimensions of the system tools are analyzed. The supply dimensions include training supply, capital investment, facility establishment, entrepreneurship support, and government procurement. Demand-based tools include management methods, legal types, and other aspects. Environmental type includes financial support. Through the investigation, we can find that there are many supply tools used in the initial stage, but after entering the development period, the choice of tools becomes wider, which shows that using different dimension tools in different periods can improve the efficiency of innovation and entrepreneurship. The dimensions of tools have increased, and the methods have also increased on a large scale. From 1998 to 2004, the proportion of innovative and entrepreneurial tools was 3.77%, from 2006 to 2012, 29.56%, and from 2014 to 2020, 66.67%. From the data, it can be seen that the proportion of innovative and entrepreneurial tools for college students is increasing year by year, indicating that modern tools provide convenience for college students’ double creation system, as shown in the following table.Table 1
Dimension table of college students’ innovation and entrepreneurship tools.
The dimension of college students’ innovation and entrepreneurship tools1998-20042006-20122014-2020Supply typeTraining supply31820Capital investment135Facility establishment014Entrepreneurship support158Government procurement003Outsourcing003Demand typeBusiness barrier036Entrepreneurial exchange037Legal management025Environmental typeFinancial support113Preferential tax0023Strategic measures01119Total647106Proportion3.77%29.56%66.67%As shown in Figure5, according to the time period of double creation system, it is divided into three time periods, namely, the initial period, the development period, and the all-round development period. In 1998-2004 and 2006-2012, supply tools used redundant demand tools and environmental tools, but after 2014-2020, college students’ innovation and entrepreneurship used more environmental tools than supply tools, which shows that with the development of the times, environmental tools play a positive role in college students’ innovation and entrepreneurship. Demand-based tools have been increasing year by year since 1998, and the growth is relatively stable, which shows that demand-based tools are indispensable for innovation and entrepreneurship, as shown in the following figure.Figure 5
Tool dimension diagram.


## 4.2.2. Analysis on the Evolution of Innovation and Entrepreneurship Tools

As shown in Figure6, comparing the total effectiveness of tools with the average effectiveness of tools, it can be seen that from 1998 to 2008, the total effectiveness of tools was about 200, of which 220 was in 2006, and 450 was in 2010 and 2020, reaching the peak. With the increase of years, the effectiveness of tools also increases slowly. It can be seen from the average effectiveness of tools that it reached a peak of 1400 around 2010. With the increase of years, the use of tools has become an indispensable way for college students to innovate and start businesses, as shown in the following figure.Figure 6
Tool analysis diagram.


## 4.3. Test of SD Innovation and Entrepreneurship Model of Computational Intelligence

By comparing college students’ innovation and entrepreneurship, preparing entrepreneurs and starting entrepreneurs, this paper analyzes and tests the SD model of computational intelligence. It can be seen from Table2 that from 2012 to 2020, the number of college students who innovate and start businesses and prepare entrepreneurs and start businesses is increasing. Prepare entrepreneurs to be 563 in 2020; start-ups will reach the best of 189 in 2020. The maximum error of innovation and entrepreneurship for college students is 5.78%, the maximum error of preparing entrepreneurs is 9.19%, and the maximum error of starting entrepreneurs is 5.26%. From the experimental results, we can see that the SD model of computational intelligence has great benefits for the system optimization of double creation system, and the test results are normal, as shown in the following table.Table 2
SD model innovation and entrepreneurship test form.
YearCollege students’ innovation and entrepreneurshipPrepare entrepreneursEntrepreneurial starterActual valueAnalog valueError valueActual valueAnalog valueError valueActual valueAnalog valueError value20122702815.21%2572808.95%57605.26%20142802904.55%299290-3.01%1121174.46%20162852985.78%362351-3.03%130126-3.08%20182993042.09%479435-9.19%1671701.80%20203003093.75%5635691.66%1891911.06%According to the relevant data in Table2, the maximum error value is about 9%, lower than 10%, which belongs to the normal error range. Through the test of SD model, we can conclude that SD model has a good effect on the optimization of college students’ innovation and entrepreneurship system and can effectively guide double creation system.


### 4.3.1. Simulation Results of SD Model under Benchmark Condition

As shown in Figure7, the simulation results of SD model under the benchmark situation show that since 2012, double creation system, preparation for entrepreneurship, and startup are on the rise. Therefore, SD model can be used to optimize the innovation and entrepreneurship system and design of college students under computational intelligence and create a better innovation and entrepreneurship system to promote more college students’ innovation and entrepreneurship, as shown in the following figure.Figure 7
SD renderings of reference conditions.The advantage lies in the fact that when the three dimensions of capital investment, experience, and education degree of innovation and entrepreneurship change accordingly, the SD model lies in the accuracy and recall rate, and the values on F1 are stable and high, so it has better optimization performance.


## 4.3.1. Simulation Results of SD Model under Benchmark Condition

As shown in Figure7, the simulation results of SD model under the benchmark situation show that since 2012, double creation system, preparation for entrepreneurship, and startup are on the rise. Therefore, SD model can be used to optimize the innovation and entrepreneurship system and design of college students under computational intelligence and create a better innovation and entrepreneurship system to promote more college students’ innovation and entrepreneurship, as shown in the following figure.Figure 7
SD renderings of reference conditions.The advantage lies in the fact that when the three dimensions of capital investment, experience, and education degree of innovation and entrepreneurship change accordingly, the SD model lies in the accuracy and recall rate, and the values on F1 are stable and high, so it has better optimization performance.


## 4.4. Comparison of Optimization Models of Innovation and Entrepreneurship of College Students with Computational Intelligence under Three Dimensions

This paper discusses the comparison of innovation and entrepreneurship models of computational intelligence college students from three dimensions: double creation system capital investment, double creation system experience, and double creation system education level. According to relevant survey data, the impact of these three dimensions on innovation and entrepreneurship is shown in the following figure, of which investment funds account for up to 45%. Without sufficient capital investment, no matter how good the innovation and entrepreneurship plan is, it cannot be implemented, followed by the level of education, accounting for 30%. Only with a good level of education can we have newer and more perfect ideas to support double creation system; finally, it is the sense of experience, accounting for 25%. Only by understanding the double creation system mechanism and experiencing the double creation system process can we better cope with the difficulties and risks faced in innovation and entrepreneurship, as shown in Figure8.Figure 8
Dimension map of innovation and entrepreneurship impact.


### 4.4.1. Investment in Innovation and Venture Capital Increased by 10%

As shown in Table3 and Figure 9, after improving the capital investment dimension of innovation and entrepreneurship, GW model, FIT model, PBL model, and SD model proposed in this paper are compared in four aspects: Precision, recall rate, accuracy, and F1. As shown in the following table, when the capital investment is increased by 10%, the optimization performance of SD model is the best, with accuracy of 0.8761, recall rate of 0.9563, accuracy rate of 0.8972, and F1 of 0.8773. Secondly, FIT model has better optimization performance, while GW model has poor optimization effect.Table 3
Comparison of models under the change of capital investment.
ModelPrecisionRecall rateAccuracyF1SD model0.87610.95630.89720.8773GW model0.85270.93380.72310.8543FIT model0.86970.93250.87190.8763PBL model0.86870.93590.75430.8681Figure 9
Model comparison diagram.


### 4.4.2. The Experience of Innovation and Entrepreneurship Has Increased by 10%

As shown in Table4 and Figure 10, when the experience of innovation and entrepreneurship increases by 10%, it can be seen from the following table combined with the following figure that the SD model proposed in this paper takes precedence over other models in terms of accuracy, recall rate, accuracy rate, and F1, followed by FIT model and GW model, and the optimization efficiency of PBL model is poor when the experience of innovation and entrepreneurship changes.Table 4
Comparison table of models under the change of experience.
ModelPrecisionRecall rateAccuracyF1SD model0.86600.94620.88710.8672GW model0.84270.91380.87010.8443FIT model0.84970.92950.87550.8463PBL model0.84870.92490.79430.8681Figure 10
Model comparison diagram.


### 4.4.3. The Level of Double Creation Education Has Increased by 10%

As shown in Table5 and Figure 11, when the education level of innovation and entrepreneurship increases by 10%, the optimization effect of SD model is better than other models by combining data and images, and its accuracy, recall rate, accuracy rate, and F1 values are 0.8921, 0.9762, 0.8762, and 0.8861, respectively. Secondly, FIT model and PBL model, compared with GW model for college students’ innovation and entrepreneurship system optimization effect, is poor.Table 5
Comparison table of models under the change of teaching level.
ModelPrecisionRecall rateAccuracyF1SD model0.89210.97620.87620.8861GW model0.86520.88970.87340.8338FIT model0.87510.96550.83270.8553PBL model0.83470.96940.83210.8781Figure 11
Model comparison diagram.As we know from the above data and charts, the investment in innovation and entrepreneurship, experience, and education level all increase by 10%. The SD model proposed in this paper has a better optimization effect on double creation system. Therefore, SD double creation system optimization model with computational intelligence has more advantages than other models, which can improve efficiency and optimize the system.Accuracy is the quantity reflecting the proximity between the measured result and the true value; recall rate and precision rate are two measures widely used in the field of information retrieval and statistical classification, which are used to evaluate the quality of results. By comparing SD model with GW, FIT, and PBL model in precision, recall rate, accuracy, and F1 performance under three dimensions, it is known that when the three dimensions are changed, the precision, recall rate, accuracy, and F1 values of SD model are higher than other models, which shows that SD model has better performance for innovation and entrepreneurship system optimization.


## 4.4.1. Investment in Innovation and Venture Capital Increased by 10%

As shown in Table3 and Figure 9, after improving the capital investment dimension of innovation and entrepreneurship, GW model, FIT model, PBL model, and SD model proposed in this paper are compared in four aspects: Precision, recall rate, accuracy, and F1. As shown in the following table, when the capital investment is increased by 10%, the optimization performance of SD model is the best, with accuracy of 0.8761, recall rate of 0.9563, accuracy rate of 0.8972, and F1 of 0.8773. Secondly, FIT model has better optimization performance, while GW model has poor optimization effect.Table 3
Comparison of models under the change of capital investment.
ModelPrecisionRecall rateAccuracyF1SD model0.87610.95630.89720.8773GW model0.85270.93380.72310.8543FIT model0.86970.93250.87190.8763PBL model0.86870.93590.75430.8681Figure 9
Model comparison diagram.


## 4.4.2. The Experience of Innovation and Entrepreneurship Has Increased by 10%

As shown in Table4 and Figure 10, when the experience of innovation and entrepreneurship increases by 10%, it can be seen from the following table combined with the following figure that the SD model proposed in this paper takes precedence over other models in terms of accuracy, recall rate, accuracy rate, and F1, followed by FIT model and GW model, and the optimization efficiency of PBL model is poor when the experience of innovation and entrepreneurship changes.Table 4
Comparison table of models under the change of experience.
ModelPrecisionRecall rateAccuracyF1SD model0.86600.94620.88710.8672GW model0.84270.91380.87010.8443FIT model0.84970.92950.87550.8463PBL model0.84870.92490.79430.8681Figure 10
Model comparison diagram.


## 4.4.3. The Level of Double Creation Education Has Increased by 10%

As shown in Table5 and Figure 11, when the education level of innovation and entrepreneurship increases by 10%, the optimization effect of SD model is better than other models by combining data and images, and its accuracy, recall rate, accuracy rate, and F1 values are 0.8921, 0.9762, 0.8762, and 0.8861, respectively. Secondly, FIT model and PBL model, compared with GW model for college students’ innovation and entrepreneurship system optimization effect, is poor.Table 5
Comparison table of models under the change of teaching level.
ModelPrecisionRecall rateAccuracyF1SD model0.89210.97620.87620.8861GW model0.86520.88970.87340.8338FIT model0.87510.96550.83270.8553PBL model0.83470.96940.83210.8781Figure 11
Model comparison diagram.As we know from the above data and charts, the investment in innovation and entrepreneurship, experience, and education level all increase by 10%. The SD model proposed in this paper has a better optimization effect on double creation system. Therefore, SD double creation system optimization model with computational intelligence has more advantages than other models, which can improve efficiency and optimize the system.Accuracy is the quantity reflecting the proximity between the measured result and the true value; recall rate and precision rate are two measures widely used in the field of information retrieval and statistical classification, which are used to evaluate the quality of results. By comparing SD model with GW, FIT, and PBL model in precision, recall rate, accuracy, and F1 performance under three dimensions, it is known that when the three dimensions are changed, the precision, recall rate, accuracy, and F1 values of SD model are higher than other models, which shows that SD model has better performance for innovation and entrepreneurship system optimization.


## 5. Conclusion

In order to promote the participation of college students’ innovation and entrepreneurship, computational intelligence is combined with double creation system, and SD model of computational intelligence is used to optimize the system of college students’ innovation and entrepreneurship. By analyzing the literature, tools, and evolution of double creation system, we can choose a better double creation system. Through the combination of computational intelligence and double creation system to optimize the double creation system, we know that the SD computational intelligence optimization model proposed in this paper has the best optimization effect and efficiency when the three-dimensional changes of double creation system capital investment, experience, and education degree. In order to enhance college students’ cognition and participation in innovation and entrepreneurship, we should give priority to the double creation system of college students with computational intelligence under SD model to optimize the system.In the future research, we should focus on the combination of SD model and computational intelligence innovation and entrepreneurship system for college students, so as to optimize the double creation system and attract more college students to join in innovation and entrepreneurship.



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*Source: 7611529-2022-04-18.xml*