Person re-identification (ReID) is a technology that utilizes artificial intelligence to solve public safety application problems such as border inspection and personnel tracking. It has the ability to identify a specific person from images collected across devices. However, in person tracking and other issues, deliberate person occlusion and complex scene environment occlusion greatly increases the difficulty of person re-identification. An improved person re-identification network PT-Net based on ResNet50 network was proposed, which combined with pose estimation and Transformer models to improve the person re-identification ability under occlusion conditions. The existing pose estimation method was utilized to detect key-points in the input image, and combined the key-point information with the person feature maps to generate a pose based person feature representation. Then, the Transformer model was used to encode the pose-based person feature representation for feature alignment and fusion. Based on the internationally available dataset Occluded-Duke, the experimental validation was conducted. The results show that the PT-Net method improves its mean average precision (mAP) and similarity ranking Rank-1 increase by 1.3, 1.5 percentage points compared to the baseline model, respectively, verifying the effectiveness and superiority of the method.