# The Application of PSO-SVM Algorithm in the Evaluation System of Sports Competition Events

Authors: Zhenglei Zhang
Journal: Security and Communication Networks
 (2022)

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## Abstract

With the development of society and the improvement of residents’ living standards, sports competition events have attracted much attention as a large-scale event that is popular with the people. Based on the process of holding sports competitions, this paper obtains 14 evaluation indicators from four dimensions of policy, social environment, economy, and natural environment, uses the PSO-SVM algorithm for a comprehensive evaluation, and finally selects Henan Province as the empirical research object. Empirical research shows that the PSO-SVM algorithm has certain applicability in evaluating SCE and can provide a reference for the government and event organizers. The evaluation index system and evaluation model constructed in this paper have certain practical value, but the training data are insufficient and the model still needs to be further improved.


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## Body

## 1. Introduction

With the steady and rapid growth of the national economy and the gradual improvement of people’s consumption level, sports competition events (SCEs) are becoming more and more frequent and their influence has become wider [1]. Paying attention to sports competitions has become one of the important contents of public cultural life. As the economic benefits and social value of sports competitions are more and more recognized by the public in the modern society, the service pattern of sports competitions needs to be further opened. SCE is not only a project to cultivate competitive talents but also an important means to realize national sports [2]. SCE is a systematic project with large investment, high output, and long-lasting operation. At the same time, the event can attract a large number of domestic and foreign audiences during its holding period, which is also a huge boost to the catering industry, tourism industry, retail industry, transportation industry, and communication industry [3]. The evaluation of SCE needs to be regarded as a project, and its measurement standard is not limited to economic benefits but also comprehensively the social benefits and resource consumption of the project, as well as the subsequent environmental protection. The evaluation of SCE needs to be based on a comprehensive index, which should be used as a reference for future SCE.In terms of the evaluation indicators of SCE, scholars mostly evaluate sports competitions from the level of the Olympic Games. Xu et al. [4] believe that the sustainable development strategic framework can create infrastructure, ecological environment, organizational governance, and culture for Olympic cities. Gordziejko [5] pointed out that the infrastructure built during the London Olympics brought new vitality to London’s tourism industry and provided a reference for its low-carbon lifestyle, which in turn promoted the development of the city. Shin [6] pointed out that large-scale sports events such as the Olympic Games demonstrate the ability of cities to promote urban development through positive economic impacts, including the image of the city or country, the increase in employment opportunities, and the rapid development of tourism. At the same time, it also promotes the renewal progress of the host city, improves the living environment and aesthetic appearance, and achieves sustainable economic growth [7]. The construction demand of the venue is one of the important contents of the overall planning of the Games [8]. Therefore, in the SCE evaluation index, the policies, economy, and ecological environment related to the venue construction are very important [9]. In the early stage of SCE, it is necessary to coordinate the venue and urban development planning and use the venue after the competition as a catalyst for urban transformation. Urban construction can also achieve improvements in infrastructure, housing, and cultural space during the construction of the venue [10]. It can be seen that the evaluation of SCE needs to be carried out from the early, middle, and late stages of the activity, and the evaluation direction should include the future development policy of the city, economic development, and ecological environment protection.The traditional evaluation methods all use linear regression or estimation methods of descriptive statistics that can be observed at the sample level and use traditional statistical techniques to conduct investigations, including univariate statistical methods, factor analysis, discriminant analysis, and Logit model to achieve evaluation normality and independence of indicators [11]. Scholars have also carried out some research work on sports-related resource optimization and sports project evaluation. Li et al. [12] used the fuzzy evaluation method to evaluate the situation of physical education and used social network and other resources to achieve effective sharing. Zhang et al. [13] used random forest and decision tree algorithms to evaluate related attributes such as school sports information and student learning data. Most of the above evaluation methods evaluate a certain research object from a subjective perspective, and the accuracy and scientificity need to be improved. This paper analyzes the principle and characteristics of the SVM algorithm through the retrieval of the relevant literature [14, 15] and decides to use the PSO algorithm to optimize the SVM algorithm [16].This paper takes the policy, society, economy, and ecological environment of the SCE host city as the first-level evaluation index and constructs the second-level evaluation index, respectively, fully considering the current situation and sports development of the SCE host city. According to the social development situation, the SCE evaluation index system is constructed, the evaluation results are divided into four grades, and the PSO-SVM algorithm is used for empirical research. The overall evaluation results provide suggestions and references for the holding of SCE.


## 2. Evaluation Index System of SCE

Project evaluation is a systematic project, and its evaluation index system includes social equity, social justice, mutual adaptability, and sustainable development between the project and the region. When determining the host city of the Olympic Games, the International Olympic Committee needs to accurately and comprehensively analyze the overall impact of the Olympic Games on the host city and even the host country in four aspects: environment, society, culture, and economy. When evaluating sports competitions, it is necessary to start from the early, middle, and late stages of the activity. Sports competitions can not only bring wonderful performances to the audience but also bring considerable economic benefits and the development of related industries. The evaluation of sports events should comprehensively consider economic benefits, resource conservation, and environmental protection. Therefore, the evaluation principles of sports events should include systematic, objectivity, efficiency, and standardization. The development and operation of large-scale sports competitions must follow the viewpoint of system theory, so the evaluation should be carried out from four aspects: policy environmentA1, social environment A2, economic benefit A3, and ecological environment A4.The index system of SCE is to concretize and use various indexes, so that the evaluation can be measurable. Sports evaluation is not a single-factor judgment work; it is multidimensional, and it is a process in which multiple factors interact and influence each other. After the initial determination of the evaluation indicators, the Delphi method was used many times to modify and improve and it was clarified that the first-level indicators of multimedia classroom teaching evaluation included policy environment evaluation, social environment evaluation, economic benefit evaluation, and ecological environment evaluation. The indicator system is shown in Figure1, and the detailed explanation is shown in Table 1.Figure 1
Evaluation index system of SCE.Table 1
Explanation of the SCE evaluation index.
IndexExplanationA11 National guidelines and policiesThe development of sports economic projects needs to comply with national or regional policies and guidelines, in order to be able to adapt to the development of societyA12 The degree of conformity with urban construction needsInfrastructure construction not only needs to meet the requirements of the competition project but also needs to meet the living needs of the residents after the competitionA13 The degree of conformity with regional economic developmentSports competitions need to meet social and economic development and need to meet the needs of residents for the level of economic developmentA21 The employment rate of residentsSports competitions will bring more jobs to residents and increase the employment rate of residentsA22 The cultural and entertainment level of residentsResidents’ consumption level in sports and the popularity of sportsmanshipA23 The population density of the regionPopulation density of cities hosting sports eventsA24 The living standards and habits of residentsMainly refers to the residents’ physical quality and physical exercise habitsA31 Financial internal rate of returnRefers to the discount rate when the sum of the present value of the financial net cash flows of each year of the project is equal to zero throughout the calculation periodA32 Sports event debt ratioThe discount rate that makes the economic net present value of a major sporting event equal to zero over the calculation periodA33 Economic net present value rateIt is a ratio indicator of the sum of the net present value of the national economy of the investment project and the present value of all investments discounted at the social discount rateA34 Economic internal rate of returnThe discount rate at which the cumulative economic net present value over the project calculation period equals zeroA41 Comprehensive index of saving natural resourcesRefers to the realization of resource conservation and environmental protection by formulating scientific plans during the eventA42 Benefit of land developmentBenefits of the project to the rational development and utilization of surrounding landA43 Benefit of comprehensive utilization of natural resourcesWhether the utilization of resources maximizes the saving of nonrenewable resourcesThe policy factors of sports competitions are the basic factors of social evaluation. The infrastructure built for the competition project must first meet the development needs of the country and the city and coordinate with economic development. Therefore, the evaluation indicators in terms of policies include the degree of conformity with national guidelines and policiesA11, the degree of conformity with urban construction needs A12, and the degree of conformity with regional economic development A13.The social environment evaluation of sports competitions is an important part of the comprehensive evaluation. According to the project evaluation theory, the evaluation of sports competitions should not only consider the macronecessity of project development but also consider the necessity of onlookers. As an important project of a country or a city, large-scale sports events must be considered from a macroperspective and microperspective and the goals of economic growth and fair distribution must be achieved through the construction of the project. The efficiency goal of sports competition projects requires that investment in various venues and facilities can be increased and national income can be increased. The fairness requirements of sports competitions should necessitate that the increased national income of the project can be reasonably distributed among different income classes, different regions, and investment in consumption. By holding large-scale events, a large number of jobs will be created for different types of labor and more consumption opportunities will be created for the high-income class. Therefore, the evaluation indicators of social links include the employment rate of residentsA21, the cultural and entertainment level of residents A22, the population density of the region A23, and the living standards and habits of residents A24.Economic benefits are the direct impact of sports competitions on social development. The development of sports competitions can effectively promote the development of regional economy, sectoral economy, and national economy. In terms of economic benefit evaluation, it can be divided into financial evaluation and national economic evaluation. In terms of financial evaluation, it is mainly inspected from financial internal rate of returnA31 and sports event debt ratio A32. In terms of national economic evaluation, it is based on economic net present value rate A33 and economic internal rate of return A34.Eco-environmental protection is a strategic policy that urban development needs to adhere to all the time. Evaluation of the ecological environment can comprehensively enhance the sustainable development capacity of local areas, provide a clean and neat environment for the development of the project, and ensure that the event will not have a negative impact on the environment during the event. The ecological environment is mainly investigated from three aspects: the comprehensive index of saving natural resourcesA41, the benefit of land development A42, and the benefit of comprehensive utilization of natural resources A43. The comprehensive index of saving natural resources refers to the need to use the minimum energy to ensure the smooth development of the entire project during the project development process, such as wastewater discharge, sewage treatment, and power consumption. The land development benefit refers to the impact of the project on the surrounding environment and area, that is, whether the project has driven the rational development and utilization of the surrounding land. The benefits of comprehensive utilization of natural resources include the comprehensive utilization of water, electricity, oil, gas, and solar energy.The above-given evaluation index system can more comprehensively summarize the actual situation in the development of SCE. Taking the evaluation index data of a city’s SCE as the input sample of the evaluation model, the evaluation effect of the model can be achieved, and it can provide a reference for the city to host large-scale SCE.


## 3. The Evaluation Model of SCE

Due to the multifactor coupling and nonlinear characteristics of SCE and its main controlling factors, it is difficult to give accurate results using conventional prediction methods. Therefore, this paper constructs the evaluation model of SCE with the help of the improved support vector machine model with strong robustness and nonlinear mapping ability.


### 3.1. Evaluation Principle of SVM

Support vector machine (SVM) is a supervised machine learning algorithm. In essence, SVM avoids the traditional process from induction to deduction, realizes efficient “transduction reasoning” from training samples to forecast samples, and greatly simplifies the usual problems of classification and regression. The final decision function of SVM is determined by only a small number of support vectors, and the complexity of the computation depends on the number of support vectors rather than the dimensionality of the sample space, which in a sense avoids the “dimensionality disaster.” A few support vectors determine the final result, which can not only help us catch the key samples and eliminate a large number of redundant samples but also represent better simplicity and robustness.SVM transforms the original linear inseparable low-dimensional space training set samples into high-dimensional spaceG through nonlinear mapping ψ and at the same time constructs the optimal hyperplane to maximize the edge of the classification interval, thereby realizing sample classification. Assuming that within the accuracy ε, the training sample set can be linearly fitted with the following equation:(1)fx=ωψx+b,where x and fx are the input and output vectors, respectively; x=x1,x2,…,xnT and fx=f1,f2,…,fnT, in which xi∈Rl,fi∈R; n is the number of samples; l is the dimension of the input vector; ω is the weight vector; ψx is a nonlinear mapping; and b is the bias term. In general, the constraints are shown in the following equation:(2)fi−ωψxi−b≤ε+μi,αψxi+b−fi≤ε+μi∗,where μi and μi∗ are slack variables used to coordinate the fitting error and i=1,2,…,n.According to the maximum interval principle of SVM, the nonlinear regression problem to be solved is the problem of minimizing the objective function, that is,min1/2ωT⋅ω+c/n∑i=1nμi+μi∗, where c > 0 is the penalty factor. And, the larger the c, the greater the penalty for misclassified samples. Using dyadic theory, this can be transformed into the corresponding dyadic problem, as shown in the following equation:(3)max−12∑i,j=1nαi−αi∗αj−αj∗Kxi,xj−ω∑i=1nfiαi−αi∗,where αi and αi∗ are the l-dimensional support vectors to be solved, and the constraint is ∑i=1nαi−αi∗=0, αi, αi∗∈0,c. In addition, Kxi,xj is the kernel function.The core of the support vector machine prediction model lies in the selection of the kernel function, and the more commonly used radial basis kernel function (RBF) is chosen in this paper. At this point, the pairwise problem becomes a quadratic programming problem and the optimal solutionsαi and αi∗ can be obtained by solving the quadratic programming problem. Therefore, the linear regression function model can be expressed as the following equation:(4)fx=∑i=1nαi−αi∗Kxi,xj+b.When using SVM of radial basis kernel function to evaluate SCE, it is necessary to give the optimal penalty factorc and kernel function parameter g, which is the focus and difficulty of using the SVM model for prediction.


### 3.2. Evaluation Principle of PSO Algorithm

The particle swarm optimization (PSO) algorithm is proposed based on the characteristics related to predation of group animals such as birds and fish and has a strong parameter-finding capability. The PSO algorithm uses massless particles to model social survival rules. Each particle represents an individual, and each particle shares with the whole particle swarm after searching the optimal position in the neighborhood, the optimal particle is used as the local optimal solution of the whole particle swarm, and the optimal solution of the whole particle swarm is obtained after iteratingT times. The algorithm is simple and fast to solve and is very suitable for the optimization of complex solving processes such as SCE evaluation. In order to overcome some limitations of the SVM model, the PSO algorithm is chosen in this paper to accelerate the merit-seeking parameters, make the evaluation process more efficient, and enhance the objectivity of the evaluation results.Suppose that in theD-dimensional search space, m particles form the population γ=γ1,γ2,…,γk. Each particle represents the position in the D-dimensional search space position in the space, which is also a potential solution. Among them, for the u-th particle γu=γu1,γu2,…,γuDT, the corresponding fitness value is calculated according to the fitness function and its speed Vu=Vu1,Vu2,…,VuDT. Individual extreme value is Pu=Pu1,Pu2,…,PuDT. The global extreme value is Pg=Pg1,Pg2,…,PgDT. In the process of iteration, the particle updates its velocity and position by individual and global extremes, as shown in the following equations:(5)Vudpp+1=wVudpp+c1r1Pudpp−γudpp+c2r2Pgdpp−γudpp,(6)γudpp+1=γudpp+Vudpp+1,where pp is the current iteration number; u=1,2,…,k; c1 and c2 are acceleration factors, which are nonnegative constants; r1 and r2 are random numbers distributed between [0, 1]; and w is the inertia weight.The basic flow of the PSO algorithm is as follows:Step 1: initialize the population, set basic parameters such as maximum genetic timesT, inertia weight w, and acceleration factors c1, c2, etc.Step 2: calculate the fitness function of the particle based on the objective function, and calculate the local optimal solution of the particle population for the current case.Step 3: optimize the velocity and position of the particles in the population according to the local optimal solution.Step 4: determine whether the termination condition is met, and terminate the iteration if the requirement is met; otherwise, return to Step 2.


### 3.3. The PSO-SVM Evaluation Model for SCE

The values of the two parametersc and g in SVM are very important. Here, c is the penalty coefficient; too high a value of c tends to overfit; too small a value of c tends to underfit. g is a parameter that comes with RBF as the kernel function. The size of the kernel parameter g is inversely proportional to the number of vectors supported by RBF. On this basis, this paper takes the influencing factors of SCE as the input vector of the evaluation model; with the ability of PSO algorithm to constantly update and self-learn, the optimal penalty factor and kernel function parameters of SVM are obtained and the SCE evaluation model is constructed, so as to effectively select SCE. In order to predict the classification more accurately and shorten the training time of the model, the K-CV method is used for cross-validation to obtain the optimal c and g. The accuracy in the sense of the training set is used as the fitness function in the PSO algorithm, as shown in Figure 2.Figure 2
Calculation process of the PSO-SVM evaluation model.


## 3.1. Evaluation Principle of SVM

Support vector machine (SVM) is a supervised machine learning algorithm. In essence, SVM avoids the traditional process from induction to deduction, realizes efficient “transduction reasoning” from training samples to forecast samples, and greatly simplifies the usual problems of classification and regression. The final decision function of SVM is determined by only a small number of support vectors, and the complexity of the computation depends on the number of support vectors rather than the dimensionality of the sample space, which in a sense avoids the “dimensionality disaster.” A few support vectors determine the final result, which can not only help us catch the key samples and eliminate a large number of redundant samples but also represent better simplicity and robustness.SVM transforms the original linear inseparable low-dimensional space training set samples into high-dimensional spaceG through nonlinear mapping ψ and at the same time constructs the optimal hyperplane to maximize the edge of the classification interval, thereby realizing sample classification. Assuming that within the accuracy ε, the training sample set can be linearly fitted with the following equation:(1)fx=ωψx+b,where x and fx are the input and output vectors, respectively; x=x1,x2,…,xnT and fx=f1,f2,…,fnT, in which xi∈Rl,fi∈R; n is the number of samples; l is the dimension of the input vector; ω is the weight vector; ψx is a nonlinear mapping; and b is the bias term. In general, the constraints are shown in the following equation:(2)fi−ωψxi−b≤ε+μi,αψxi+b−fi≤ε+μi∗,where μi and μi∗ are slack variables used to coordinate the fitting error and i=1,2,…,n.According to the maximum interval principle of SVM, the nonlinear regression problem to be solved is the problem of minimizing the objective function, that is,min1/2ωT⋅ω+c/n∑i=1nμi+μi∗, where c > 0 is the penalty factor. And, the larger the c, the greater the penalty for misclassified samples. Using dyadic theory, this can be transformed into the corresponding dyadic problem, as shown in the following equation:(3)max−12∑i,j=1nαi−αi∗αj−αj∗Kxi,xj−ω∑i=1nfiαi−αi∗,where αi and αi∗ are the l-dimensional support vectors to be solved, and the constraint is ∑i=1nαi−αi∗=0, αi, αi∗∈0,c. In addition, Kxi,xj is the kernel function.The core of the support vector machine prediction model lies in the selection of the kernel function, and the more commonly used radial basis kernel function (RBF) is chosen in this paper. At this point, the pairwise problem becomes a quadratic programming problem and the optimal solutionsαi and αi∗ can be obtained by solving the quadratic programming problem. Therefore, the linear regression function model can be expressed as the following equation:(4)fx=∑i=1nαi−αi∗Kxi,xj+b.When using SVM of radial basis kernel function to evaluate SCE, it is necessary to give the optimal penalty factorc and kernel function parameter g, which is the focus and difficulty of using the SVM model for prediction.


## 3.2. Evaluation Principle of PSO Algorithm

The particle swarm optimization (PSO) algorithm is proposed based on the characteristics related to predation of group animals such as birds and fish and has a strong parameter-finding capability. The PSO algorithm uses massless particles to model social survival rules. Each particle represents an individual, and each particle shares with the whole particle swarm after searching the optimal position in the neighborhood, the optimal particle is used as the local optimal solution of the whole particle swarm, and the optimal solution of the whole particle swarm is obtained after iteratingT times. The algorithm is simple and fast to solve and is very suitable for the optimization of complex solving processes such as SCE evaluation. In order to overcome some limitations of the SVM model, the PSO algorithm is chosen in this paper to accelerate the merit-seeking parameters, make the evaluation process more efficient, and enhance the objectivity of the evaluation results.Suppose that in theD-dimensional search space, m particles form the population γ=γ1,γ2,…,γk. Each particle represents the position in the D-dimensional search space position in the space, which is also a potential solution. Among them, for the u-th particle γu=γu1,γu2,…,γuDT, the corresponding fitness value is calculated according to the fitness function and its speed Vu=Vu1,Vu2,…,VuDT. Individual extreme value is Pu=Pu1,Pu2,…,PuDT. The global extreme value is Pg=Pg1,Pg2,…,PgDT. In the process of iteration, the particle updates its velocity and position by individual and global extremes, as shown in the following equations:(5)Vudpp+1=wVudpp+c1r1Pudpp−γudpp+c2r2Pgdpp−γudpp,(6)γudpp+1=γudpp+Vudpp+1,where pp is the current iteration number; u=1,2,…,k; c1 and c2 are acceleration factors, which are nonnegative constants; r1 and r2 are random numbers distributed between [0, 1]; and w is the inertia weight.The basic flow of the PSO algorithm is as follows:Step 1: initialize the population, set basic parameters such as maximum genetic timesT, inertia weight w, and acceleration factors c1, c2, etc.Step 2: calculate the fitness function of the particle based on the objective function, and calculate the local optimal solution of the particle population for the current case.Step 3: optimize the velocity and position of the particles in the population according to the local optimal solution.Step 4: determine whether the termination condition is met, and terminate the iteration if the requirement is met; otherwise, return to Step 2.


## 3.3. The PSO-SVM Evaluation Model for SCE

The values of the two parametersc and g in SVM are very important. Here, c is the penalty coefficient; too high a value of c tends to overfit; too small a value of c tends to underfit. g is a parameter that comes with RBF as the kernel function. The size of the kernel parameter g is inversely proportional to the number of vectors supported by RBF. On this basis, this paper takes the influencing factors of SCE as the input vector of the evaluation model; with the ability of PSO algorithm to constantly update and self-learn, the optimal penalty factor and kernel function parameters of SVM are obtained and the SCE evaluation model is constructed, so as to effectively select SCE. In order to predict the classification more accurately and shorten the training time of the model, the K-CV method is used for cross-validation to obtain the optimal c and g. The accuracy in the sense of the training set is used as the fitness function in the PSO algorithm, as shown in Figure 2.Figure 2
Calculation process of the PSO-SVM evaluation model.


## 4. Experimental Results and Analysis

In this paper, the SCE of large games in Henan Province is selected as the research object, the basic data collection is carried out according to the SCE evaluation index system summarized above, the actual development of SCE of large games and the evaluation of SCE by relevant personnel are analyzed, and the selectable degree of SCE and 200 data samples can be obtained for model construction and testing. For the convenience of computing, we divided the overall data into 8 datasets, each containing 25 experimental data.To avoid blind search and time-consuming efforts, this paper places certain restrictions on the relevant parameters of the PSO-SVM model for SCE evaluation. The penalty factorc is taken in the range of [0,100], and the kernel function parameter g is taken in the range of [0,1]. The initial population size is 20, the initial global search ability parameter is 1.5, the initial local search ability parameter is 1.8, and the number of iterative evolutions is 2000. Through model training, the optimal penalty factor is 1.242 and the optimal kernel function parameter is 0.337.To verify the reasonableness of the PSO-SVM model proposed in this paper for SCE evaluation, we choose SVM without the optimization algorithm and the commonly used machine learning method random forest (RF) model for accuracy comparison. All data will be used for each evaluation method, and the final accuracy comparison results are shown in Table2 and Figure 3.Table 2
The accuracy of different evaluation methods.
DatasetAccuracy (%)PSO-SVMSVMRF197.4993.2190.34296.8292.3388.53396.4593.1989.23493.9791.0587.44594.6789.3390.25695.6687.8289.32795.4889.5787.32896.1587.4289.97Figure 3
The accuracy of different evaluation methods.The results in Table2 and Figure 3 demonstrate the superiority of the data in this paper, with average accuracies of 95.84%, 90.49%, and 89.05% for the three methods, respectively. The accuracy rates of the three methods are all above 85%, which is quite convincing. But in comparison, SVM is less stable. In addition, the optimization effect of the PSO algorithm for SVM is well reflected, and the average accuracy rate is increased by 5.35%. The evaluation accuracies of SVM and RF are similar, and PSO-SVM has a greater advantage over them.Figure4 reveals the difference in the total training plus testing time for the different evaluation methods. The overall difference in runtime between RF and SVM is not significant, with RF running slightly faster by 3.6 ms on average over the 8 datasets. The use of PSO makes the SVM run 12.2 ms faster on average, which means the efficiency is increased by almost 55.2%. This is a good indication of the excellent optimization capability of PSO and also shows that in the sports industry, much research can be done on the basis of SVM to obtain evaluation models with higher accuracy.Figure 4
Comparison results of training time of different models.The SCE evaluation model in this paper can provide some reference in event selection for the 14th Provincial Games to be held in Henan in 2022. Through research and interviews with people related to the event holding, this paper evaluates some SCEs that are widely popular and vastly discussed. We use the equal-width method to determine the final score of sports events, with a minimum score of 1 and a maximum score of 5. Finally, we divide the ratings into a total of four grades (C, B, A, and S), and S on behalf of this event is very much recommended to choose. The scores of the ten major sports related to the provincial games calculated by the PSO-SVM method are shown in Table3 and Figure 5.Table 3
SCE rating scale.
Track and fieldBadmintonTable tennisTennisSwimmingGradeASSBBRowingWrestlingTriathlonKarateSkateboardGradeAABAAFigure 5
SCE rating scale.Overall, due to economic development and the enthusiasm of the people, the competitive sports environment has flourished and many sports have broken through the limitations of venues and gained the support of more citizens. Badminton and table tennis, with their wide participation and high popularity, are rated S and are very suitable for SCE. Track and field, wrestling, and karate are not as popular as the above ball sports but require fewer demanding venues and have a higher number of participants, thus receiving an A rating. Tennis, swimming, and triathlon, on the other hand, require a high level of human physical fitness and have more restrictions on venues and equipment. According to the evaluation index system proposed in this paper, they are not well suited to the lifestyle habits of local residents, resulting in a B rating. However, these sports are highly spectator-friendly and have a certain economic potential. Their development in the local community can be supported by improving publicity and creating a better economic environment.


## 5. Conclusion

In order for the public to actually feel the charm of competitive sports, it is necessary to scientifically select the projects of the major games. There are many factors affecting SCE selection, complex sources and complex nonlinear relationships between factors and evaluation results, so this paper investigates the PSO-SVM evaluation model to accurately characterize SCE. In this paper, a comparative experimental approach is used, and the results show that RF and SVM have similar evaluation results, while PSO improves the accuracy of SVM by 5.35% and speeds up the running time by 55.2%. Therefore, it also demonstrates that the evaluation model in this paper is sufficiently convincing. The evaluation model is applied to the SCE selection for the Henan Provincial Games and yields a rating of S for badminton and table tennis, which are by far the highest rated sports. However, swimming, tennis, and triathlon all have a lot of room for improvement, which confirms the generalizability of the model.A good model can help the evaluation to be carried out with half the effort, and now, there are many optimization algorithms and a lot of research on SVM models. We hope to combine more cutting-edge algorithms afterwards to bring the model accuracy to a higher level and be able to put the scale of sports events to the whole country.



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*Source: 6865425-2022-04-28.xml*