Due to some complex factors (e.g., occlusion), extracting stronger feature representation in person re-identification remains a challenging task. In this paper, we proposed a novel self-supervision and supervision combining transformer-based person re-identification (ReID) framework, namely SSSC-TransReID. Different from the general transformer-based person ReID models, we designed a self-supervised contrastive learning branch. For training it, we proposed a novel Random Rectangle Mask to simulate the real occlusions, so as to enhance the feature representation for occlusion. Finally, we utilized the joint-training loss function to integrate the advantages of supervised learning with ID tags and self-supervised contrastive learning without negative samples, which can reinforce the ability of our model to extract stronger discriminative features, especially for occlusion. Extensive experiments on several benchmark datasets show our proposed model outperforms the state-of-the-art ReID methods by large margins on the mean average accuracy (mAP) and Rank-1 accuracy.