Aiming at the problem of insufficient accuracy of multilabel classification of human action at present, a multilabel classification method of human action in the rope skipping scene is proposed. It realizes feature recognition and classification by collecting human action features in the scene of skipping rope movement and uses RNN to optimize the human action feature recognition algorithm. On the basis of feature recognition, the characteristics of human movement in the rope skipping scene are classified, the confidence map of the key point position is obtained by using the Gaussian modeling method, and the action multilabel classification is realized. Finally, experiments show that the multilabel classification method of human action in rope skipping scene has high accuracy and fully meets the research requirements.

Since the table tennis mixed doubles competition was officially listed as the Olympic Games, the players around the world paid more attention to the project. In this background, the 20 mixed doubles finals were used in the literature, video observation, and analysis of multiple regression. From the receiving point of view, the score difference between men and women is not very great, but female players may be more consistent. The contribution of male and female scores to the game is more effective than model 1 and model 2 for different rounds. Therefore, model 2 is more efficient in the analysis of high-level table tennis competitions. Multiple regression model can be used to analyze and predict table tennis singles, doubles, and mixed doubles games, which we will see more and more in future research results.