Aiming at the problem of person re-identification (person re-ID) caused by complex background and object occlusion,angle transformation and pedestrian posture change in real environment,a person re-identification model based on efficient channel attention (ECA) and poly-scale convolution (PSConv) is designed.Firstly,the residual network is used to extract the global features,and a feature fusion module based on PSConv and ECA is added at the end of the network.The global features are fuzed with the global features extracted from the module to get a new global feature,and then the new global feature is segmented to obtain local features.Finally,the new global feature and the local feature are fused to get the final feature, and the loss function is calculated.The experiment is verified on Market1501 and DukeMTMC-reID data set.Rank-1 and mean average precision reach 94.3 % and 85.2 % respectively on Market1501 data set,and 86.3 % and 75.4 % respectively on DukeMTMC-reID data set.The results show that the model can deal with the complex situation in the actual environment,enhance the discrimination of pedestrian features,and effectively improve the accuracy and precision of pedestrian recognition.