The traditional unsupervised domain adaptive person re-identification algorithm suppressed the noise of pseudo-label poorly and lack inter-domain generalization ability. For the above problems, an unsupervised domain adaptive person re-identification algorithm was proposed which based on soft pseudo-label and multi-scale feature reconstruction. In order to suppress pseudo-label noise, the predicted value of the parallel network is used as the soft tag, and pseudo-label noise is corrected by cross-proofreading methods, which provides a more robust soft false tag for unsupervised domain adaptive tasks. In order to enhance the generalization ability between domains, multi-scale feature reconstruction and Hadamard product feature fusion methods are used to process the deep and shallow feature layer information, realize the style conversion from source domain data to target domain, and solve the problem of poor adaptability of residual network domain with instance normalization and batch normalization network, so as to enhance the generalization ability of the network to source domain and target domain. Experimental results show that the proposed algorithm has achieved good performance in both Market to Duke and Duke to Market unsupervised domain adaptive tasks, which is significantly better than the related algorithms.