Performance Prediction and Action Performance Analysis of Sports Competitive Events Based on Deep Learning

Abstract

Physical education performance in primary and secondary school classroom education seems mediocre, and many students treat it as a minor subject. With parents and schools paying attention to physical education, physical education performance is very important now, whether in primary and secondary schools or universities. Nowadays, college students have less and less physical exercise, and sports achievements are one of the index achievements for evaluating scholarships. Many teachers also arrange appropriate physical training reasonably in order to improve students' sports achievements. Forecasting sports achievements is the key to making scientific sports training plans. According to the study of college students' group sports achievements, we can predict students' follow-up learning achievements, collect, sort out, and study students' sports achievements information regularly, so as to better guarantee the quality of college sports teaching. This paper compares and analyzes the sports achievements of various schools from the aspects of strength, endurance, and sensitivity. At the same time, it compares and analyzes the sports achievements of major universities. Finally, from the perspective of physical education, this paper analyzes and studies the situation of individual schools and then finds out the pedagogical factors that make them achieve excellent physical education results and puts forward the teaching strategies to improve physical education results, as well as the positive influence of physical education teachers on teaching, school physical education results, and students' physical quality. When teaching courses, use big data mining technology to find qualified students to study the actual teaching needs, recommend an efficient learning curriculum system to students with reference to research conclusions, and give rich material resources. Teachers can teach students in accordance with their aptitude with reference to their usual learning situation, instead of "one size fits all."