Physical education (PE) is, in general, one of the most important skills developed for human healthiness. Many barriers exist in society to improve the performance in Chinese physical activities. Furthermore, the incorporation of 5G communication network technology is becoming a trend in the increase of physical activity in China on a daily basis. Physical exercise may assist Chinese people to enhance their mental abilities, self-concept, and goal orientation and avoid mental illnesses such as sadness and anxiety. Physical exercise without education is like having a body but no soul. There is no doubt about the value of physical education and other types of exercise in the educational system. In this paper, we propose refined physical education teaching based on 5G network technology to obtain everlasting data without termination. First, we preprocess the sports dataset using a stacked denoising autoencoder (SDAE), and a Gaussian Mixture Model (GMM) is utilized for the feature extraction process. A random forest approach (RFA) is then used in the selection of the features. Furthermore, we adopted a CNN-based upgraded classifier for classification and efficient data allocation (EDA) algorithm for storing data generated by the 5G network. Experimental results reveal that our proposed method outperforms the baseline methods by a huge margin.
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