An unsupervised person re-dentification (ReID) method was proposed to solve the insufficient representation of person features and the noisy labels generated by the clustering process in the process of unsupervised ReID,which jointed feature refinement and noise-tolerant comparative learning.Firstly,a non-local channel refinement module (NCRM) was designed to enrich the unlabeled person representation by weighted reinforcement of key feature information,which fused the important features of non-local channel to capture the discriminative representation between classes of unlabeled data to form more discriminative feature descriptors.Secondly,generalized mean (GEM) pooling adaptive adjustment parameters were used to enhance the ability of extract information from different fine-grained regions to accomplish full expression of characteristics.Then,a noise-tolerant dynamic contrastive equalization (DCE) loss was designed for unsupervised associated learning to mitigate the negative impact of noisy label on the network.Finally,the experimental results on two public datasets verify the effectiveness and advancement of the proposed method.The mAPreaches 83.1 % and 71.9 % respectively,which is superior to other advanced methods.