In order to solve the problem of labeling difficulty in video-based person re-identification dataset, a neighborhood center iteration strategy based on one-shot video-based person re-identification is proposed in this study, which gradually optimizes the network by using pseudo-labeled tracklets to obtain the best model. Aiming at the problem that the accuracy of predicting pseudo labels of unlabeled tracklets is low, a novel label evaluation method is proposed. After each training, the center points of each class in the features of the selected pseudo-labeled tracklets and labeled tracklets are used as the measurement center points for predicting the pseudo labels in the next training. At the same time, a loss control strategy based on cross entropy loss and online instance matching loss is proposed in this study, which makes the training process more stable and the accuracy of the pseudo labels higher. Experiments are implemented on two large datasets: MARS and DukeMTMC-VideoReID, and the result demonstrates that the proposed method outperforms the current state-of-the-art methods.