In this study, we propose a one-sample person re-identification method, which adopts a progressive learning framework in the process of iteration in order to making full use of the characteristics of labeled data and unlabeled data to optimize the model. In this framework, we iteratively train convolutional neural network to update the model and utilize multiple-model training together to select the reliable pseudo-label data during label estimation. Then, we update training data for the next round of training. The training data is splited into three parts: labeled data, pseudo-labeled data, and indexed-labeled data. We set up the corresponding loss function for each set of data and update the CNN model by the joint training on the three parts. In the progress of iteration, the pseudo-labeled data and index-labeled data are constantly updated. Under the one-sample set, rank-1=65.3, mAP=45.6. When the rate of labeled data is increased to 40%, rank-1=83.8, mAP=64.9. The result indicates that the semi-supervised person re-identification method proposed in this study can provide excellent results comparable to the supervised learning method with less labeled data, which fully demonstrates the effectiveness of the method.