The domain of Cloth-Changing Person Re-identification has garnered substantial attention recently owing to its pivotal role in security applications. The essence of Cloth-Changing Person Re-identification lies in acquiring information unaffected by clothing variations, such as gait and silhouette. Most studies focus on the Convolutional Neural Network models. However, main drawback of Convolutional Neural Network is the limitation of receptive field. Methods based on Convolutional Neural Network primarily focus on modeling local information, making it complex to extract the discriminative part of multiple spatial contexts. Therefore, we proposed a Vision Transformer-based Cloth-Changing Person Re-identification framework called TransRL-ReID. Specifically, we encoded an image as a sequence of patches and constructed a Vision Transformer-based framework. In addition, a module named DropAll was designed to reduce local bias by adaptive adjustment of attention weights for each patch. We conducted extensive experiments on the real-world benchmark PRCC dataset. TransRL-ReID achieves 66.7% on Rank-1 and 60.0% on mean Average Precision, outperforming previous Convolutional Neural Network-based methods. After visual analysis of the results, we found that TransReID could extract information unrelated to clothing, such as gait and silhouette, and paid less attention to clothing information. DropAll improves the model’s ability to fit pedestrian distributions.