Research on Artificial Intelligence Assisted Decision Making System in Higher Level Sports Training

Abstract

From the perspective of process monitoring of sports training, this paper discusses the basic framework and decision-making function system composition for establishing a decision-making system for higher vocational sports training. Integrate students’ sports data to design a real-time monitoring program for sports training intensity, which forms the mapping relationship between physical fitness and exercise intensity. Utilize and improve the Apriori algorithm to create an association rule mining model for the physical fitness of higher vocational students. Propose using the Multi-Agent model to optimize the data information management of the decision-making system and combine it with the RBF neural network algorithm for sports training performance prediction. To analyze the impact of the sports training decision-making system, application examples are introduced. Examine how association rules are implemented in the decision-making system of higher vocational sports training. When the number of frames is 300, the error correction accuracy before and after the improvement of the Apriori algorithm is 79.14% and 94.23%, respectively. The feedback rate of students’ performance information is maintained at about 0.85 with the assistance of the sports training decision-making system improved by the algorithm. The improved sports training decision-making system is more practical.

Keywords

• apriori algorithm
• rbf neural network
• multi-agent model
• decision-making system
• sports training
• 68p30