The invention relates to a pedestrian re-identification method based on a depth multi-loss fusion model. According to the method, a deep learning technology is used, pre-processing operations such asoverturning, cutting, random erasing and style migration are carried out on the training set picture. Feature extraction is carried out through a basic network model. A plurality of loss functions areused for carrying out fusion joint training on the network. Compared with a pedestrian re-identification algorithm based on deep learning, since the method uses a plurality of preprocessing modes, fusion of three loss functions and an effective training strategy, the pedestrian re-identification performance on the data set is greatly improved. On one hand, multiple preprocessing modes can expanda data set, the generalization capability of the model is improved, the occurrence of an overfitting condition is avoided, and on the other hand, the three loss functions have respective advantages and disadvantages, and when the three loss functions are effectively combined, the used model can obtain a better recognition result.