Person re-identification (Re-ID) is a research hotspot in the field of computer vision, it is the re-identification of the same person under different surveillance devices. Features from different parts of the person are independently extracted by most existing Re-ID models, the intrinsic connections between human body parts are not taken into account, the recognition rate is relatively low when pedestrians have multi-pose and viewpoint changes. Graph neural network (GNN) possess strong feature modeling capabilities for graph-structured data, the associated information between various nodes in the graph can be fully mined by GNN, thus, problems such as pedestrian occlusion, viewpoint and posture changes can be effectively solved to a certain extent. The key issues and technical bottlenecks in the current Re-ID field were first systematically sorted out. Core challenges including viewpoint changes and occlusion interference were covered. Then, the theoretical framework of GNN was outlined. On this basis, combined with international cutting-edge progress, the technical paths through which GNN enhance the correlation of pedestrian features were deeply analyzed. Meanwhile, the optimization strategies of existing methods were summarized. Finally, the future development directions of GNNs and person Re-ID technology were proposed.