Analysis of Tai Chi Ideological and Political Course in University Based on Big Data and Graph Neural Networks

Abstract

Tai Chi martial arts education is one of the components of school education. Its educational value is not only to require students to master basic Tai movement technical skills and improve their physical fitness but also to bring students’ ideological progress and cultivate students to respect teachers and lectures. Excellent moral qualities such as politeness, keeping promises, observing the rules, and acting bravely, as well as the cultivation of the spirit of unity and cooperation, and the quality of will also have a certain meaning. However, the scientific Tai Chi ideological and political courses and the construction of Wude education interactive classrooms lack relevant research. Therefore, this article builds a Tai Chi ideological and political interactive classroom system based on big data technology and graph neural network. First, the spatio-temporal graph convolutional neural network is used to reason about the relationship between Tai Chi action categories and strengthen the low-dimensional features of semantic categories and their co-occurrence expressions used for semantic enhancement of current image features. In addition, in order to ensure the efficiency of the Tai Chi scene analysis network, an efficient dual feature extraction basic module is proposed to construct the backbone network, reducing the number of parameters of the entire network and the computational complexity. Experiments show that this method can obtain approximate results, while reducing the amount of floating-point operations by 42.5% and the amount of parameters by 50.2% compared with the work of the same period, and achieves a better balance of efficiency and performance. Secondly, based on the big data of historical Tai Chi classrooms, this article constructs an interactive classroom system that can effectively improve the quality of Tai Chi ideological and political courses.