In order to solve the problem of huge labeling cost for person re-identification， a method of one-shot video-based person re-identification with multi-loss learning and joint metric was proposed. Aiming at the problem that the number of label samples is small and the model obtained is not robust enough， a Multi-Loss Learning （MLL） strategy was proposed. In each training process， different loss functions were used for different data to optimize and improve the discriminative ability of the model. Secondly， a Joint Distance Metric （JDM） was proposed for label estimation， which combined the sample distance and the nearest neighbor distance to further improve the accuracy of pseudo label prediction. JDM solved the problems of the low accuracy of label estimation for unlabeled data， and the instability in the training process caused by the unlabeled data not fully utilized. Experimental results show that compared with the one-shot progressive learning method PL （Progressive Learning）， the rank-1 accuracy reaches 65.5% and 76.2% on MARS and DukeMTMC-VideoReID datasets when the ratio of pseudo label samples added per iteration is 0.10， with the improvement of the proposed method of 7.6 and 5.2 percentage points， respectively.