Convolutional neural network-based recognition method for volleyball movements

With the development of network technology and computer intelligent monitoring technology, a large number of video data came into being. In view of the analysis of specific targets in video, the traditional artificial analysis method can not meet the existing needs. In volleyball teaching in college physical education, because each student has different movements, intelligent processing of video data has become a key issue. Through collecting and sorting out the relevant research results on behavior recognition, it is found that in the research of deep learning, the algorithm structure applied in this paper is representative, and its strong learning ability, especially compared with the traditional algorithm, can more accurately identify human movements, natural language processing and so on, but the research on volleyball action recognition is still less. Therefore, this paper constructs a data set, improves the convolution neural network model, subsequently, new models are constructed through the neural network structure to improve the accuracy of the nonlinear expression and optimize the content of the input data. In order to more accurately analyze the effectiveness of this algorithm, the new data are obtained by grouping the volleyball games in college sports courses. Compared with the original paper, the accuracy of the improved 3D network is improved by 3.3%–88.5%, and the complexity is reduced by 33.6%.