In order to solve the problem of low accuracy of Transformer encoder caused by the loss of person image blocks information and insufficient expression of person local features in person re-identification， an improved Transformer encoder and feature fusion algorithm for person re-identification is proposed. This algorithm uses relative position encoding to solve the problem that Transformer will lose the relative position information of person image blocks during attention operation so that the network can focus on the semantic feature information of person image blocks， thus enhancing the ability to extract pedestrian features. Secondly， the local patch attention module is embedded into the Transformer network to weighted strengthen the local key feature information and highlight the significant features of the person area. Finally， the fusion of global and local information features is used to achieve complementary advantages between features and improve the recognition ability of the model. In the training stage， Softmax and triple loss functions are used to jointly optimize the network. The proposed algorithm is experimentally compared and analyzed on the mainstream datasets of Market1501 and DukeMTMC-reID. The Rank-1 accuracy reaches 97.5% and 93.5% respectively， and the mean average precision （mAP） reaches 92.3% and 83.1% respectively. The experimental results show that the improved Transformer encoder and feature fusion algorithm can effectively improve the accuracy of person re-identification.