Intelligent Optimization Algorithm for the Selection and Training Path of “One School, Multiple Products” Reserve Talents in Changde City under the Perspective of Athletic Sports

Double line pyramid structure is the middle core area into a pyramid structure outside the outline also into a pyramid structure style, can be applied to the talent echelon in the sort of talent, its specific structure as shown in Figure 1 [23]. The theoretical structure of the diagram consists of three layers, the middle core area from top to bottom for the top talent, mid-level talent and grass-roots talent, respectively, in the illustration by A0, B0 and C0. The two sides outside the core area are also composed of three layers, from top to bottom are top reserve talents, middle reserve talents and grass-roots reserve talents, which are represented by A1, B1 and C1 respectively in the diagram.

Figure 1.

The structure of the double line pyramid

The double-barred pyramid structure model should satisfy the following mathematical logic, i.e. A1 ≥ A0, B1 ≥ B0, C1 ≥ C0, i.e. the number of reserve talents in each tier must be greater than or equal to the number of talents on board in that tier. The double-barred line area of each tier is mainly supplied by the core area of the next tier, with a small portion supplied by external introduction. The bottom layer, i.e. the grass-roots talents are mainly supplied by the grass-roots reserve talents, and a small part of them are introduced from the outside, and the grass-roots reserve talents mainly come from the introduction of talents. The mathematical logic established in this way completes the establishment of the dual-line pyramid structure model.

“One School with Multiple Products” reserve personnel training refers to the dynamic process of the synergistic interaction between sub-systems and their actors within the competitive sports reserve personnel training system that affects the orderly evolution of the system as a whole under the impetus of the system [24].This process must follow the basic evolutionary law of synergy from initiation, operation to implementation on the ground, involving a series of issues such as system-driven elements, the interaction of multiple subjects, and how synergistic cultivation can work concretely in practice.Based on the basic evolution law of synergistic cultivation, taking into account the advantages of the system and its intrinsic connection with the sub-systems in the system of cultivating athletic reserve talents, the evolution mechanism of “One School with Multiple Products” reserve talents cultivation is shown in Fig. 2, whose key elements include synergistic drive, synergistic operation and synergistic landing.

Figure 2.

The development mechanism of reserve personnel culture

The mechanism of synergistic drive reflects what kind of driving mechanism the synergistic interaction between the subsystems within the competitive sports reserve talents training system is generated under. The mechanism of synergistic operation reflects how the actors within the subsystems of sports, education, social market and individual family conduct evolutionary games and make corresponding behavioral strategy choices to form the system’s inherent structural relationships and operational guidelines under the driving force of the mechanism. The mechanism of synergistic promotion reflects how the structure, relationship and rules of the system, such as synergistic driving force and synergistic operation, are applied to the reality of the synergistic cultivation of athletic sports reserve talents. The three mechanisms reflect the basic evolution law of the initiation, operation and implementation of the collaborative cultivation of athletic sports reserves, and the analysis of the synergistic driving mechanism, synergistic operation mechanism and synergistic promotion mechanism will provide the theoretical basis for the subsequent research.

Complex adaptive systems theory is one of the theories of complex systems science that organically links macro and micro aspects to study the mechanisms of complexity generation and system emergence in complex systems. In the micro aspect, the research object is defined as a subject with adaptability, which accumulates experience through nonlinear interaction with the environment and changes its own structure to improve its own behavior in order to adapt to environmental changes. At the macro level, the theory of complex adaptive systems emphasizes that the system composed of adaptive subjects evolves and develops through the process of inter-subject interaction.

The core idea of complex adaptive systems is that “adaptation creates complexity”. It can be understood that the “adaptability” of the subject creates the “complexity” of the system [25].Among them, the adaptability of the subject is manifested in the adjustment of its own behavioral style based on environmental information. It is the interaction of the subject that improves the survivability of the whole system, and the adaptive behavior of the subject is the intrinsic motivation for the evolution of the complex adaptive system. The subject has the initiative to continuously interact with the surrounding environment during the evolution of the system, accumulate experience through the feedback of its own behavioral results, modify its own behavioral rules, and seek its own maximum adaptability. The subject is capable of learning, and its learning process ranges from weak to strong, from simple to complex. Within the subject, the cognitive model exists in various ways, which can be changed through random or conscious behavior, and the process of “selecting-formulating-retaining” is adopted to realize the survival of the fittest.

Suppose there are m subjects in a “One School with Multiple Products” training program M , M = {1,2,3,⋯,i,⋯,m}, M denoted by (M,S), where S is the number of connections. Let the cooperation between the participating subjects be ij , when i, j is no longer cooperating, denoted as S \ {ij}, D_i denotes the connection between i and other sets, and when i is not cooperating with any set, D_i = 0.

i has two strategies σ_i to choose from at any point in time, either to continue to cooperate, i.e., σ_i = 1, or to stop cooperating, i.e., σ_i = 0. σ_–i then represents the strategy choices of the subjects in the network other than i.

Participation in the “One School with Multiple Products” program has a cost, which is assumed to be c, and c has a range of values. If j cooperates with i, then j provides a units of resources for the whole “excellence program”, obviously for m subjects, a < c < ma. If δ_i represents the endowment of i, then δ_i0 is obviously the initial endowment of i, and δ_i is the endowment of the first period.

θ_i is the learning ability of i. By joining the “One School with Multiple Products” program, the endowment of i becomes δ_i1 = δ_i0 + θ_i[a ∑σ_j + γ], where γ is the endowment of i for something else.

Record the general interest of all S subjects as b_i, and the number of subjects with i have contact with the number of cooperation is b₂ , of which the number of cooperators to maintain cooperation is K_ic, the number of no longer cooperate with the number of K_id . Then b₂ / k_i will be expressed as the proportion of subjects with the same strategy as the subject i.

Finally, assuming the reward and punishment mechanism in the “One School with Multiple Products” reserve personnel training program, the reward for each S of each i is e. Through the above assumptions, we can get the benefit when subject i adopts cooperation: (1) Πi(σi=1,σ−i,R)=eki+b1kic+b2kic/ki+θi[a(∑σj+1)+γ]−c ${{\Pi }_{i}}({{\sigma }_{i}}=1,{{\sigma }_{-i}},R)=e{{k}_{i}}+{{b}_{1}}{{k}_{ic}}+{{b}_{2}}{{k}_{ic}}/{{k}_{i}}+{{\theta }_{i}}[a(\sum{{{\sigma }_{j}}}+1)+\gamma ]-c$

Taking uncooperative gains for: (2) Πi(σi=0,σ−i,R)=b1kid+b2kid/ki+θi[a∑σj+γ] \[{{\Pi }_{i}}({{\sigma }_{i}}=0,{{\sigma }_{-i}},R)={{b}_{1}}{{k}_{id}}+{{b}_{2}}{{k}_{id}}/{{k}_{i}}+{{\theta }_{i}}[a\sum{{{\sigma }_{j}}}+\gamma ]\]

When Π_i(σ_i = 1,σ_–i,R) ≥ Π_i(σ_i = 0,σ_–i,R), i.e., c ≤ θ_ia + (k_ic – k_id)(b₁ + b₂ / k_i) + ek_i, i ‘s optimal strategy is to cooperate. That is to say, when the benefits of i participating in the “One School with Multiple Products” reserve personnel training program are greater than the costs, i will participate. The stronger the learning ability θ_i of subject i, the greater the benefit; at the same time, the greater the number of participants k_ic in the network, the greater the benefit.

Assuming that the model is dynamic, then for the benefit of connecting k_i is C(k_i), when i chooses to no longer cooperate with the benefit of the past cooperation, that is, C(k_i) – C(k_i – 1) – Π_i(σ_i = 1,σ_–i,R)>0, it will no longer cooperate and will randomly cooperate with other individuals, assuming that the principle of this cooperation is “triplet formation”, that is, the probability of cooperating with neighbors is q , then the probability of cooperating with other individuals is (1 – q). And for the success of the next cooperation depends on the trust in the new partner, which is set to t, the expected return is: (3) tΠi(σi=1,σ−i,R)+(1−t)Πi(σi=0,σ−i,R) \[t{{\Pi }_{i}}({{\sigma }_{i}}=1,{{\sigma }_{-i}},R)+(1-t){{\Pi }_{i}}({{\sigma }_{i}}=0,{{\sigma }_{-i}},R)\]

In this paper, we assume that i is connected to its neighbors’ neighbors, then q = 1, t = 1, while the cost of terminating the previous relationship is α, H_i is the set of terminated relationships; the cost of establishing a new connection is β, G_i is the set of new connections.

The model reaches steady state when Π_i(σ,R) ≥ Π_i(σ,(R ∪ G_i)\H_i) – αh_i – βg_i, then: (4) Πi(σ=1,(R∪Gi)\Hi)=e(ki−hi+gi)+b1(kic−hi+gi)+b2kicgi/(ki−hi+gi)+θi[a(∑σj+1)+γ]−c,Πi(σ=0,(R∪Gi)\Hi)=b1(kid+hi−gi)+b2kidhi/(ki+hi−gi)+θi[a∑σj+γ] \[\begin{align} & {{\Pi }_{i}}(\sigma =1,(R\cup {{G}_{i}})\backslash {{H}_{i}}) \\ & =e({{k}_{i}}-{{h}_{i}}+{{g}_{i}})+{{b}_{1}}({{k}_{ic}}-{{h}_{i}}+{{g}_{i}}) \\ & +{{b}_{2}}{{k}_{ic}}{{g}_{i}}/({{k}_{i}}-{{h}_{i}}+{{g}_{i}})+{{\theta }_{i}}[a(\sum{{{\sigma }_{j}}}+1)+\gamma ]-c\;, \\ & {{\Pi }_{i}}(\sigma =0,(R\cup {{G}_{i}})\backslash {{H}_{i}}) \\ & ={{b}_{1}}({{k}_{id}}+{{h}_{i}}-{{g}_{i}})+{{b}_{2}}{{k}_{id}}{{h}_{i}}/({{k}_{i}}+{{h}_{i}}-{{g}_{i}})+{{\theta }_{i}}[a\sum{{{\sigma }_{j}}}+\gamma ] \\ \end{align}\]

In the previous section, this paper introduced a framework for talent evolution based on the pyramid structure. Since the multi-objective optimization problem involves several sub-objectives, these sub-objectives constrain each other and even contradict each other. Therefore, for the multi-objective optimization problem, this chapter proposes a population stratification mechanism and competition strategy based on the pyramid structure. Firstly, the population is graded by the fast non-dominated sorting method, then the individuals within each level are sorted in ascending order according to the fitness value, and finally stratified according to the pyramid structure on the basis of the proportion of individuals in each level.

In order to transport the discovered excellent individuals to the upper layer, and in order to avoid the whole population falling into local optimality, the layer adopts a diversity strategy, i.e., the generation of new individuals needs to be sufficiently randomized. Therefore, this paper defines the rule for the generation of new individuals in the exploration layer as follows: (5) X(n+1)=X(n)+r0×δ \[X(n+1)=X(n)+{{r}_{0}}\times \delta \;\] where X(n + 1) denotes a new individual generated after the n nd iteration, X(n) denotes an existing individual before the n th iteration, and δ is any random number in [-1, 1]. r_o is a constant representing the search radius of individuals in the exploration layer. All individuals in the exploration layer generate a new population based on the iterations, using a competition that directly eliminates old individuals and retains new ones, and then passes individuals to the previous layer according to the competitive strategy of single elite roulette.

The passing layer is a bridge between the exploration layer and the exploitation layer, with both cultivation and screening abilities, and the randomness of the passing layer is given some limitations. After completing the cultivation operation, all individuals in the passing layer are iteratively updated, and new individuals are generated in the following manner: (6) X(n+1)=X(n)+r×δ \[X(n+1)=X(n)+r\times \delta \]

where X(n + 1),X(n) is the same as defined in the previous section, and r denotes the search radius, which decreases as the number of iterations increases, as shown in the following equation. i.e: (7) r=r0×αn \[r={{r}_{0}}\times {{\alpha }^{n}}\] Where r_o is the initial radius, α is the convergence factor, the purpose is to limit the randomness of the transfer layer, in this paper we take α = 0.99, n is the number of iterations.

Since in the current generation sub-population, each sub-objective has its corresponding individual to make it reach the current optimal and sub-optimal. Therefore, in the acceleration operation of new individuals, there are multiple excellent individuals to haul them, and the individual hatching formula of this layer is: (8) X=X(n+1)+∑i=1mλj(Xj1−Xj2) \[X=X(n+1)+\sum\limits_{i=1}^{m}{{{\lambda }_{j}}({{X}^{j1}}-{{X}^{j2}})}\] where X,X(n + 1) is defined as in the previous section, X^j1,X^j2 denotes the individual that optimizes and sub-optimizes the j rd sub-objective in the current generation, respectively, and λ_j denotes the weight of each sub-objective function on the acceleration of the individual.

The core task of the mining layer is to deeply mine the region around the excellent individuals, update the individuals in the layer according to Eq. (5), and then perform competitive operations on the new and old individuals and the individuals transmitted up.

The whole evolution process follows the exploration layer, the transmission layer, and the mining layer sequentially from bottom to top, and finally the optimal frontier surface is found through competition and collaboration.

In any individual delivery process, the layer that outputs the individuals is called the delivery layer, and the layer that receives the individuals is called the receiving layer. First, the individuals in the conveying layer are sorted according to the fitness value from low to high, and the probability that each individual may be selected is calculated separately through the single elite roulette strategy, and then a certain number of individuals are selected to be conveyed to the upper layer according to the promotion ratio of each layer.

For the individuals promoted up from the transmission layer, the transmission layer carries out cultivation operations on them. For the multi-objective optimization problem, the cultivation operation is: (9) X(n+1)=Xt+R×δ \[X(n+1)={{X}_{t}}+R\times \delta \] (10) X=X(n+1)+∑i=1mλj(Xj1−Xj2) \[X=X(n+1)+\sum\limits_{i=1}^{m}{{{\lambda }_{j}}({{X}^{j1}}-{{X}^{j2}})}\] where X(n + 1) denotes a new individual generated by X_t in a domain of radius R. The rest of the parameters are defined as in the previous section. This operation improves the efficiency of inter-layer collaboration by giving promoted individuals the opportunity to adapt to the environment instead of directly competing in the new environment.

The collaboration strategy is mainly based on a pyramid structure with a clear division of labor, where the number of individuals in each layer increases sequentially from the top to the bottom, and the focus of the task is shifted from exploitation to exploration. Therefore, the transfer ratio between layers follows the principle of gradual increment from top to bottom.

After obtaining the intra-layer competition and inter-layer collaboration strategies based on the pyramid structure, this paper introduces an improved adaptive genetic algorithm to solve the multi-objective optimization model for the evolutionary model of “One School with Multiple Products” reserve talent cultivation in competitive sports [26]. The improvements of adaptive genetic algorithm are as follows:

Based on the adaptive genetic algorithm designed in the previous section, the steps for solving the optimization problem of “One School with Multiple Products” reserve talent cultivation model are as follows:

1) Set the size of the population, the initial iteration number, the maximum iteration number, the number of selected individuals, the retention probability of elite individuals, the crossover probability and the probability of variation.

The solution flow of adaptive genetic algorithm is shown in Fig. 3.

Figure 3.

Adaptive genetic algorithm solution process

In order to verify the effectiveness of the adaptive genetic algorithm designed in this paper in solving the evolutionary model of “One School with Multiple Products” reserve talent cultivation, PSO-GA algorithm and the standard GA algorithm are selected for the control test. The GA algorithm uses the uniformly sorted selection method for selection, the simulated binary crossover method for crossover, and the polynomial mutation for mutation, which are the best performers among the conventional algorithms, and the optimal individual retention method is used as the elite selection strategy, while the PSO-GA algorithm uses the PSO-optimized selection, crossover, and mutation strategies. The three algorithms use the same coding scheme with fitness function design and Figure 4 shows the iterations of the different algorithms.

Figure 4.

Iterative scenarios of different algorithms

As can be seen from the figure, the standard GA algorithm reaches convergence roughly around 450 generations, while the PSO-GA algorithm reaches convergence roughly around 520 generations, and the adaptive genetic algorithm proposed in this paper tends to converge roughly around 280 generations. Although the standard GA algorithm convergences quickly, the optimal fitness value is far inferior to that of this paper and the PSO-GA algorithm, so the advantage of fast convergence speed does not have practical significance. The improved adaptive genetic algorithm in this paper not only converges faster, but also obtains higher optimal solutions, because the improved crossover operator and mutation operator in this paper can provide enough perturbation in the late iteration process, so that the algorithm can jump out of the local optimal solution and continue to search for the global optimal solution. The comparison algorithm fails to do so and thus enters a premature state.

The fitness function value of the algorithm result is the score of the algorithm for solving the “One School with Multiple Products” talent cultivation evolution, which is divided into 8 different types of objectives, and the total score can be evaluated by comparing the total score with each score. By analyzing and comparing the total score and each score, the advantages and disadvantages of the intelligent optimization algorithm can be evaluated.Table 1 shows the scores of different algorithms for solving the objective. As can be seen from the table, the total score of “One School with Multiple Products” reserve talent cultivation of the improved adaptive genetic algorithm proposed in this paper is higher than that of the standard genetic algorithm and PSO-GA genetic algorithm.This is because in the algorithm proposed in this paper the various operators have been improved in terms of solution accuracy, which can search for the reserve training program with higher degree of superiority, and the improved genetic algorithm of this paper improves the superiority of the optimal solution by 7.61% and 20.74% compared with GA and PSO-GA algorithms, respectively.Therefore, the improved adaptive genetic algorithm in this paper is used to obtain the optimal path of “One School with Multiple Products” reserve talent training, which lays the foundation for enhancing the quality of reserve talent training in competitive sports.

In order to further analyze the intelligent optimization effect of the adaptive genetic algorithm in the “One School with Multiple Products” reserve talent cultivation path, the two comparative algorithms in the previous section are still selected for verification.The data related to “One School with Multiple Products” reserve talent cultivation paths of two schools A and B are selected as the basis for initializing the same parameters of the three algorithms, and then obtaining the simulation results of the examples of different universities as shown in Fig. 5. Figure 5(a)~(b) shows the simulation results of school A and school B, respectively. As can be seen from the figure, the superiority value of the adaptability of the “One School with Multiple Products” reserve talent cultivation pathway will show a decrease in the data after 140 iterations, and then there will be a certain stable value.Through this phenomenon, it shows that the optimization of feature weights will make the improved algorithm have more compact samples, and the intelligent optimization algorithm can ensure to get the optimal solution of the path of “One School with Multiple Products” reserve talents cultivation, and will not appear the situation of local optimal solution.Compared with the GA algorithm and the PSO-GA algorithm, the adaptive genetic algorithm proposed in this paper has better performance results in the optimization of the “One School with Multiple Products” reserve talent training path, and the optimal solution is used to determine the specific goals of the “One School with Multiple Products” reserve talent training path, so as to better ensure the training effect of reserve talents in competitive sports.

Figure 5.

Simulation of the training of talents

Since 2024, more than 10 schools will be trained in the “One School with Multiple Products”, reserve talent training program, and in December 2024, the physical fitness of three types of students in urban areas, suburbs and suburbs will be analyzed, and the questionnaire will be quantified by a four-point scoring table, that is, 1~4 points means “not as good as the original”, “no obvious improvement”, “improved”, “significantly improved”, and expressed by T1~T4. A total of 420 questionnaires were distributed, 400 were effectively recovered, and the obtained questionnaire data was entered into Excel for statistics. Table 2 shows the improvement of students’ physical fitness through the cultivation of “One School with Multiple Products”, reserve talents.

It can be seen from the survey that the implementation of the “One School with Multiple Products” reserve personnel training program has significantly improved the physical fitness of students.Among them, 74 teachers in urban schools believed that the physical fitness of their students had improved significantly after the implementation of the “One School with Multiple Products” Reserve Talent Development Program, 144 teachers believed that it had improved, and only 3 teachers believed that it was not as good as it had been.Twenty-three of the physical education teachers in the peri-urban schools believed that the physical fitness of their students had improved significantly after the implementation of the “One School with Multiple Products” program, and 65 believed that it had improved.Fourteen of the physical education teachers in remote schools believed that the physical fitness of their students had improved significantly after the implementation of the “One School with Multiple Products” program, and 32 believed that it had improved.Overall, up to 88% of students believe that the implementation of the “One School with Multiple Products” program has improved their physical fitness.It shows that the implementation of the “One School with Multiple Products” reserve training program can significantly improve the physical fitness of students, and the implementation of the “One School with Multiple Products” can help students to achieve physical fitness and improve their health, and lay a solid foundation for the training of reserve talents for competitive sports.

In order to more deeply analyze the reform of the school “One School with Multiple Products” reserve talent cultivation program, according to the value embodied in the construction of the overall satisfaction, physical health, sports skills, teachers, community development, sports competitions, curriculum and teaching of the construction tasks.From the above 10 schools, four schools, namely Changde No. 1 Middle School, No. 7 Middle School, No. 6 Middle School and No. 3 Middle School, were randomly selected for comparison, and SPSS software was used to analyze and compare the differences in the satisfaction level of different schools with the construction of “One School with Multiple Products” reserve talent cultivation program, and the Likert five-point scale was used, with the options of Very Satisfied, Comparatively Satisfied, General, Not Very Satisfied and Unsatisfied assigned 5 to 1 points respectively and analyzed numerically, The Likert five-point scale is used, and the options of very satisfied, relatively satisfied, average, less satisfied and dissatisfied are assigned a score of 5~1 respectively and analyzed numerically and statistically. Table 3 shows the differences in the overall satisfaction of different schools with the construction of “One School with Multiple Products” reserve personnel training program.

The data show that the four middle schools in Changde City are satisfied with the construction of the school’s “One School with Multiple Products” Reserve Talent Cultivation Program, with a water average of 4.37 points and a standard deviation of 0.442 points.It can be seen that the overall satisfaction with the construction of “One School with Multiple Products” reserve personnel training program in Changde City is at a high level, and the fluctuation of the data is relatively stable.In a one-way ANOVA, the total variance for the entire sample is equal to the between-group variance plus the within-group variance.Intergroup variation refers to the variation between the means of overall satisfaction with the construction of the “One School with Multiple Products” reserve personnel training program, i.e., the differences between the data caused by various factors.Intra-group variation refers to the variation of data within the four middle schools in Changde City on the satisfaction with the overall value of the “One School with Multiple Products” reserve talent cultivation program.According to the ANOVA results in the table, the statistic of its F-test is 3.249 and the significance Sig.=0.054>0.05.It shows that there is no significant difference in the overall satisfaction of more than 10 schools in Changde City with the construction of the “One School with Multiple Products” reserve talent training project, but there is a slight deviation in the data in the table, among which the data of Changde No. 6 Middle School is slightly lower than that of the other three middle schools, which leads to this phenomenon is inseparable from the objective and subjective factors of the construction of school sports “One School with Multiple Products”.Therefore, the development of “One School with Multiple Products” reserve personnel training in each school in Changde City can help to improve the physical quality of students, enhance the diversified development of school physical education programs, and also effectively enhance the quality of competitive sports reserve personnel training.

Changde City is rich in local sports teaching characteristics, through the “One School with Multiple Products” development model to create diversified and personalized school characteristics, enhance the school brand image, and provide students with better educational resources.Schools can take the following measures: firstly, enrich the physical education curriculum, schools can offer indoor + outdoor physical education courses, such as indoor theoretical learning, outdoor skills learning, etc., to provide students with more diversified channels to learn about the intangible cultural heritage of sports. Secondly, explore local excellent ethnic sports and cultural resources. Schools can utilize the local natural environment and social resources to organize students for participation in ethnic sports competitions and performances, thus enhancing their interest and the impact of sports ICH programs.By cultivating the teacher team, schools can strengthen the professional quality and teaching ability of the teacher-institution group for the sports ICH program through training, assessment, and incentives.Through the above measures, schools can realize the development goal of “One School with Multiple Products” and have their own unique ethnic brand. At the same time, schools can establish links with local communities, cultural groups, sports organizations, and so on, further enriching the school’s educational resources for physical education and intangible cultural heritage.

School “One School with Multiple Products” reserve talent cultivation model is an important carrier to build “sunshine sports, two hours of exercise every day, sports and art 2 + 1 program”, to promote sports work from a single sports competition to create a sports culture, and to promote the participation of objects from some teachers and students to all teachers and students. It also promotes the transformation of sports work from a single sports competition to the creation of sports culture, and promotes the change of participating objects from some teachers and students to all teachers and students.Schools should vigorously publicize the concept of “sports, happiness and health”, form a strong sports culture, guide students to participate in special sports activities, go out of the classroom, go to the playground, and go into nature.

Schools should actively set up platforms to promote student participation and increase the fun of activities, using the “big classroom activities, school season games and sports and culture festivals”as carriers. At the same time, family games are set up in the seasonal sports meets, which are held on a family basis, so that parents can also participate in them and experience the benefits of sports, thus correcting the attitudes of parents who have a biased awareness of sports and realizing the charm of sports culture. Schools can organize sports culture festivals according to their own special sports and invite other schools in the vicinity to participate in them, and during the sports culture festivals, they can be open to parents, inviting parents as guests to cheer for their children, so as to stimulate the excitement of the students, and then achieve good results in the competitions.