<p>This paper presents a novel framework for fine-grained martial arts gesture recognition that integrates attention mechanisms to enhance model accuracy.The proposed approach introduces several key innovations to improve the recognition of subtle variations in martial arts movements. First, a multiscale attention mechanism is employed, allowing the model to dynamically focus on both fine-grained body parts and global features, capturing the intricate relationships between different scales of motion. Second, structured attention maps are introduced to help the model better understand the spatial relationships between body parts, further improving recognition accuracy. Additionally, the framework enhances local feature generation, where attention mechanisms refine the model’s focus on critical areas, while attention regularization prevents overfitting by reducing excessive attention to certain body parts, boosting the model’s generalization capabilities. This novel combination of attention mechanisms results in a highly effective and accurate system for martial arts gesture recognition, achieving superior performance compared to traditional methods.</p>