In order to alleviate the problem of person re-identification task’s dependence on labeled data and low accuracy in occlusion scenarios, an unsupervised person re-identification method combining random occlusion and ContrastiveCrop is proposed. Firstly, the input image is randomly occluded with various patterns according to the real scene, and the contrast cropping method is used to generate pedestrian image samples with greater differences while ensuring the semantic consistency of the positive sample pair, so as to alleviate the impact of occlusion and complex background on the network. Then, the SC-CAResNet network was designed in the contrastive learning model for multi-granularity feature extraction, so that it can pay more attention to the important areas of the image; finally, the network was trained by combining multiple loss functions. Experimental results show that this method outperforms traditional classical methods on two public datasets of person re-identification, Market-1501 and DukeMTMC-reID, has stronger robustness, and significantly improves the ability of person re-identification in model occlusion scenes.