In response to the shortcomings of existing motion pose recognition algorithms in terms of accuracy and efficiency in human pose detection, this paper proposes an efficient detection algorithm based on an improved YOLOv8pose. This algorithm optimizes the occlusion scenario of key points by introducing the RL-SEAM module, enhances the utilization of contextual information through the C2f-Context mechanism, enhances the model"s ability to recognize complex poses, and uses the PoseSA lightweight detection head to improve the model"s effectiveness and efficiency in recognizing motion poses. The experimental results show that the improved YOLOv8pose algorithm has achieved significant improvement in human motion pose recognition tasks. Its number of parameters and model size have been reduced by 14.24 and 10.94 percentage points respectively compared to the original YOLOv8n benchmark model. At the same time, accuracy, recall, and mean average precision (mAP) have been improved by 7.60, 7.60, and 10.54 percentage points respectively compared to the original model. Therefore, the YOLOv8-LSP model proposed in this article helps to solve the challenges faced in human motion pose recognition tasks, such as key point occlusion and complex and variable postures.