Person re-identification is a very challenging image retrieval task that aims to match the specific person images from different camera views. Person re-identification model requires a large amount of training data to improve its generalization ability, however the current datasets of person re-identification are not enough that tend to make the model overfit. Therefore, some data augmentation methods are used to increase the amount of training data to improve the generalization ability of the model. Cutmix is a common data augmentation method in the field of deep learning, but it is rarely used in person re-identification task because the triple loss cannot handle the decimal similarity label generated by cutmix. In order to put the cutmix method for data augmentation in person re-identification, we extend the triplet loss that is commonly used in person re-identification to a form which can handle decimal similarity label from the perspective of optimizing image similarity. In addition, we propose Strip-Cutmix data augmentation method, which is more suitable for person re-identification, and discuss the strategies about using Strip-Cutmix in the field of person re-identification. Extensive experiments show that our approach can prevent model overfit and achieve impressive performance on DukeMTMC-ReID, Market-1501 and MSMT17 benchmark datasets.