Paying attention to the global contour and the person local details is very important for the existing person re-identification methods. In order to extract these more representative features， a person re-identification network method based on the feature Pyramid branches and the non-local attention modules is proposed to extract the global and local characterization features of person. Firstly， this method introduces a lightweight feature Pyramid branch structure， extracts features from the different network layers， and aggregates them into a two-way Pyramid structure. Secondly， in order to further improve the accuracy of person re-identification， the non-local attention module is used to extract the global features， which can not only obtain the global information of person， but also pay attention to the local details of person， so that their final fusion features are more representative. Finally， the characteristics of different layers are fused， and the joint loss function strategy is used to train the network model to significantly improve the performance of the backbone network. Through a large number of experiments on the four public person re-identification datasets， MSMT17， Market1501， DukeMTMC-ReID and PersonX， it is proved that the proposed method based on the feature Pyramid branch and the non-local attention is competitive compared with some advanced person re-identification methods.