The application of tennis teaching in computer network environment

Abstract

In this paper, students' implicit learning characteristics parameters are first transformed using BP neural network. Then the parameter weights are corrected using the activation function and normalized to obtain explicit data characteristics. Finally, a tennis online teaching platform is constructed by measuring the correlation between learning behavior data through the K-mean clustering algorithm and extracting the learning behavior regularity coefficients accurately using time series. The findings revealed that the platform had an average pass rate of 78.6% for students, and the average learning time was 6.5 hours. The teaching quality of tennis can be effectively improved in the computer network environment.