n cross-camera scenarios, it relies on the learning of label mapping relationships to improve recognition accuracy. The supervised person re-identification model has better recognition accuracy, but there are scalability problems. For example, the accuracy of algorithm identification relies heavily on effective supervised information. When adding a small amount of data in the classification process, all data needs to be reprocessed, resulting in poor real-time performance. Aiming at the above problems, an unsupervised person re-identification algorithm based on soft label is proposed. In order to improve the accuracy of label matching, first, learn soft multilabel to make it close to the real label, and obtain the reference agent by calculating the loss function of the reference data set to achieve the purpose of pre-training the reference data set. Then, calculate the expected value of the minimum distance between the generated data and the real data distribution (using the simplified 2-Wasserstein distance), calculate the mean and standard deviation vector of the soft multilabel in the camera view, and the resulting loss function can solve cross-view domain label consistency issues. In order to improve the validity of the soft tag on the unmarked target data set, the joint embedding loss is calculated, the similar pairs between different categories are mined, and the cross-domain distribution misalignment is corrected. In view of the problem that the residual network training duration and the unsupervised learning accuracy are low, the structure of the residual network is improved by combining the SENet and fusing multi-level depth feature to improve the training speed and accuracy. The experimental results show that the rank-1 and mAP are better than advanced correlation algorithms.