Aiming at the problem that it is difficult to effectively extract the key information of pedestrians in the chaotic scene and the global feature method is invalid in the case of partial occlusion,a multi-granularity person re-identification (ReID) method guided by a double pyramid structure is proposed.First,the attention pyramid in is embedded ResNet50 to guide the network to dig out features of different granularities from coarse to fine,making the network more inclined to focus on the significant areas of pedestrians in complex environments;secondly,the branch of the double attention feature pyramid (DFP) with asymmetric structure is adopted.Multi-scale pedestrian features are extracted to enrich the diversity of features.At the same time,the dual attention mechanism allows branches to capture finer-grained local features from shallow information;finally,the coarser-grained global features are merged with multi-level and fine-grained local features,The two kinds of pyramids interact to retain more discriminative multi-granularity features to improve the pedestrian occlusion problem.Experiments on multiple data sets have shown that the evaluation indicators are higher than most current mainstream models.Among them,on the DukeMTMC-reID data set,Rank-1,mAP and mean inverse negative penalty (mINP) reached 91.6%,81.9% and 48.1%,respectively.