In order to solve the problems of loss of joint point detection and inability to identify small targets in the scenario of multi-person human pose estimation, an improved YOLOv8-Pose model was proposed. The core improvement of the algorithm is that the convolution in the C2F module is replaced by the variable convolution DCNV2, which enhances the feature extraction ability of the network. At the same time, the weighted bidirectional pyramid BiFPN module is used to replace the feature fusion module in the original model, which aims to retain the small target information and fuse more shallow information to improve the recognition accuracy. Finally, in order to further strengthen the ability to capture and analyze key parts, the SimAM attention mechanism was introduced to weight the local features. Experimental results show that the detection accuracy of the algorithm reaches 70.5% on the CrowdPose dataset, which is 3.3% higher than that of the original model. Compared with the original YOLOv8-Pose model, the improved model not only has higher detection accuracy, but also has a significant improvement in the recognition effect of small targets. It can be seen that the improved network can be applied to multi-person human posture detection more accurately and effectively.