Construction and Application of Tennis Teaching and Training Prediction Model Based on Deep Learning

Abstract

Teaching tennis is a crucial component of conventional physical education. It seeks to foster in students a lifetime love of physical activity, increase academic effectiveness, and foster social growth. The concept is simplified by the usual teaching approach, which causes certain negative effects on the students' practice. A dataset of 200 college student hard-court tennis matches was used to train an algorithm. This study develops the tennis teaching and training prediction model based on Shuffled Frog Leap Integrated Regularized Artificial Neural Network (SFL-RANN) algorithm. Tennis coaching optimizes player potential through development plans, optimal training, and adaptable opponent techniques by utilizing SFL-RANN synergy. The comprehensive system of tactical diagnosis indicators for tennis that results from applying these theories comprises building principles, basic criteria, diagnosis indicator content, and assessment indicator design. A series of tests designed to validate the efficacy of the developed indication system and diagnostics model are included in the result of the paper. The outcomes show how well the SFL-RANN performed in accuracy (99.5%), recall (99.7%), and response time (45 ms) etc. To maximize tennis instruction and training, this paper attempts to assess athletes' performance through the development of a diagnostic system. This report offers observations that may result in modifications to athletic programs, particularly those about the teaching of tennis.