For the lack of training samples in pedestrian reidentification (reID) research, we propose a pedestrian re-ID method based on convolutional neural network (CNN) to improve the recognition accuracy and generalization ability. Firstly, we employ the unsupervised learning method for the generative adversarial network  to generate unlabeled images, so the training data set is expanded. Secondly, the original data set is collaborated to perform semi-supervised CNN training, and a Siamese network is constructed to perform training according to the features of the identification model and the verification model. Finally, the unlabeled image category distribution method is introduced, and the cross entropy loss is calculated to perform similarity measurement. Experiments on the Market1501, CUHK03, and DukeMTMC-reID datasets show that the proposed method has a nearly 3% to 5% improvement in performance indicators such as rank1 and mAP in comparison with the original Siamese method. The proposed method has certain application value in small sample scenarios.pedestrian re-identification;convolutional neural network (CNN);generative adversarial network (GAN);cross entropy;Siamese