Physical education teaching design under the STEAM concept using the convolutional neural network.

With the continuous progress of science and technology and the increasing complexity of tasks, traditional physical education (PE) teaching methods are becoming insufficient to meet modern research demands. This work aims to design an efficient deep learning (DL) model for PE teaching under the Science, Technology, Engineering, Arts, and Mathematics (STEAM) educational concept. Based on the convolutional neural network (CNN), this work designs a CNN-STEAM model and then evaluates and compares this model with traditional CNN and Residual Network (ResNet) models in terms of basic and prediction performance. Indicators such as accuracy, recall, F1 score, and response time are used to quantify model performance. Through extensive experiments and data analysis, it is found that the CNN-STEAM model achieves significant improvements in all performance indicators, particularly with over 20% increases in accuracy, recall, and F1 score, along with reduced response times. The main contribution of this work is the successful design and validation of an efficient CNN-STEAM model, which demonstrates excellent performance in data processing and analysis within the field of PE teaching. This achievement not only provides robust technical support for researchers and technicians in PE but also offers new insights and methods for applying DL in the domain.