Aerobics Exercise Performance Training for College Students based on Optimized Apriori Algorithm

Amazing changes have occurred in college education with the rapid development and popularization of science and technology in the information age. This paper proposes a design method for predicting the effect of college students' sports performance training based on the optimized Apriori algorithm to develop a more standardized and complete physical education course training plan. The research introduces the gravitational search algorithm (GSA), and the particle swarm optimization algorithm (PSA) combines the two algorithms into a hybrid algorithm that integrates with the Apriori algorithm to form the GSA-PSO-A algorithm to predict the performance training effect of students. The algorithm was used to find valuable associated data in the data and finally conduct application analysis. The GSA-PSO algorithm reached a stable fitness value when the number of iterations was 200 and 100 in the unimodal and multimodal function tests, respectively, and the feasibility was the best. The GSA-PSO-A algorithm proposed by the research institute could effectively mine the training data of college students' sports performance and provide a feasible path for improving the teaching of physical education courses to college students.