To address the problem of occlusion in person re-identification, which makes it difficult for the model to fully express the pedestrian information, a person re-identification method based on attention mechanism and feature fusion was proposed. Based on the ResNet-50 network, a network model combining random occlusion and multi-scale feature fusion is proposed. By applying random occlusion to the input images, it simulates the real scenario of pedestrians being occluded, thereby enhancing the robustness of the model to occlusion. The network is divided into a global branch and a local branch, where the global branch extracts global salient features, while the local branch supplements local multi-scale deep features to extract deeper person information. Combined with attention mechanism, filtering irrelevant information, mining and enhancing discriminative feature representations. The proposed method was compared with advanced person re-identification methods on two standard public datasets and an occlusion dataset, and the effectiveness of the method was confirmed through experimental verification of the comparison between Rank-1 and map with the comparison method.