The purpose of unsupervised domain adaptive person re-identification is to generalize the recognition ability of training in the source domain to the target domain to reduce the dependence on labels. At present, the network based on clustering methods will inevitably be affected by environmental noise during the clustering process, which will reduce the original recognition performance of the network. To solve this problem, the person re-identification network with pseudo label refinement based on correlation score is proposed. Firstly, by calculating the correlation scores between the top k similar sample sets of global and local features, reliable correlations between two types of features are found, so as to extract local fine-grained features that existing pseudo-label optimization methods ignore. Then, the scores are used to optimize the local pseudo-labels, reducing the network's attention to irrelevant local features of the person. In addition, relying on the correlation scores, the prediction results of the optimized local pseudo-labels are used to refine the global pseudo-labels, which alleviates noise during the clus-tering process and refines the complete representation of person features. Compared with the unsupervised domain adaptive method in recent years, the experimental results of the network on three public data sets, DukeMTMC-ReID, Market1501 and MSMT17, show that the network performance is significantly improved.