In view of the flaw that pedestrian re-identification technology is characterized with a low recognition rate due to feature occlusion in practical applications, a complex environment pedestrian defense occlusion re-identification method has thus been proposed, which consists of two parts: global and local feature extraction. Firstly, with ResNet-50 network as the backbone network, feature aware attention mechanism is used for a global feature extraction to extract global features; subsequently, the method of feature segmentation space is used for the local feature extraction in the local area; finally, feature fusion is performed through a multi-scale bidirectional pyramid network. Experiments are to be conducted on commonly used pedestrian occlusion datasets such as Market-1501, etc. which verifies the effectiveness of the proposed method and improves the effectiveness of pedestrian re-identification.