In person re-identification applications, pedestrian images are susceptible to illumination changing, similar wearing and different shooting angles, so it is difficult to distinguish sample pairs, which leads to incorrect matching. Aiming at this problem, a person re-identification optimization algorithm based on joint loss and siamese network is proposed. Firstly, the residual convolutional neural network is used to extract image features. The joint loss including focal loss and cross entropy loss is used to carry out supervised traning on the extracted features, in order to increase the models attention to the indistinguishable pairs. Then, the cosine distance is used to calculate the similarity between images to realize the pedestrian recognition. Finally, a re-ranking algorithm is adopted to reduce the mismatch rate. Experimental results on open datasets including Market-1501 and Duke MTMC-reID show that the matching rate (Rank-N) is 91.2% and 84.4%, respectively, and the mean Average Precision（mAP）is 85.8% and 78.6%.