In order to solve the problem of low accuracy of current pedestrian re-identification algorithms caused by phenomena such as pedestrian occlusion a nd pose changes,a pedestrian re-identification algorithm based on multi-level feature fusion is proposed.Firstly,the short-distance information in the image features is extr acted by the self-attention mechanism backbone network ResNeSt;secondly,the multi-scale pyramid convolution (pyconv) branch network is used to extract the associated feature in formation of the long pixels in the image to improve the expression ability of the model;fin ally,a unified form and the learnable generalized mean pooling (GEM) is used to replace the tra ditional average pooling layer for the purpose of focusing on the variability of differen t regional characteristics.The average inverse negative penalty (mINP) is added as a new e valuation metric in the testing phase.Experimental results show that the proposed algorit hm exhibits advantages on multiple datasets,with Rank-1reaching 90.9% and mAP reaching 89.8% on the DukeMTMC-ReID dataset.