Application of GGNN inference propagation model for martial art intensity evaluation

Abstract Moderate martial art exercise can enhance physical fitness and promote physical and mental health, but excessive exercise intensity may cause damage to the body. To address this issue, this study adopts the forward maximum matching method and a pre-trained conditional random field model to extract semi-structured and unstructured data to construct a knowledge graph for martial arts intensity assessment. A gated graph neural network is utilized to evaluate exercise intensity and to optimize it through the use of parallel computing. The sampling-optimized model exhibited a 0.13 ms reduction in processing time compared to the second-best model, while the model with an optimized training process demonstrated a 6.7 s reduction after 100 rounds of training, outperforming the second-best model. The evaluation accuracy of the strength evaluation model in the three groups was 81.6, 86.6, and 91.6%, respectively. The average evaluation accuracy of the optimized model was 7.2 and 13.4% higher than other models, respectively. The optimized martial art exercise intensity evaluation model has the potential to significantly reduce time consumption, thereby enhancing overall efficiency. By reducing computation time, the model can improve productivity, responsiveness, and user experience. Furthermore, it can improve evaluation efficiency and accuracy, making martial art exercise intensity evaluation more precise. Ultimately, the model can effectively assist individuals in selecting suitable exercises.