A pose-guided dual-branch clothing-changing person re-identification network (PGNet) was proposed to address the issue of reduced recognition accuracy in the clothing-changing person re-identification tasks caused by complex environments and clothing variations. The network adopted a dual-branch architecture based on appearance features and guided by pose features. To effectively remove the interference of clothing-related information, reduce its impact on model performance, and preserve the deep representational features, a multi-level feature fusion module was designed. An action-related adjacency matrix and a natural topology adjacency matrix were constructed and combined to form a dual adjacency matrix, which was input into the graph convolutional network. An adjacency matrix weighting mechanism was introduced to enhance the model’s ability to capture pose features. A bilinear multi-feature pooling method was adopted to enhance the complementarity between the pose features and the appearance features, thereby improving the recognition accuracy. Experimental results demonstrated that the PGNet achieved mAP values of 60.5%, 84.7%, 15.7%, 22.6%, and Rank-1 accuracies of 63.7%, 93.3%, 59.5%, 41.2% on the clothing-changing datasets of PRCC, VC-Clothes, Celeb-reID, and Celeb-reID-light, respectively, outperforming other comparative methods such as SirNet. The proposed method can effectively reduce the impact of clothing variations and significantly improve the recognition accuracy.