In order to solve the problem of low accuracy of person re-identification (ReID) algorithm in complex environment caused by unclear detail features and changeable attitudes of pedestrians,a ReID network based on multi-granularity feature extraction and feature fusion is proposed.Firstly,two granularity partitioning methods are used to obtain the local features of the image at the input and output ends of the backbone network.Secondly,spatial transformation network (STN) is introduced to align the global image and enhance the local image.Finally,local feature fusion is used to mine the correlation information between features to improve the model′s ability to recognize similar samples.Experimental results show that the proposed method achieves good recognition performance on multiple datasets.The mean average precision (mAP) and first accuracy (Rank-1) of the market-1501 dataset are 84.87% and 94.45%,respectively.Compared with the current mainstream ReID algorithms,the proposed method has better recognition effect.