Person re-identification (ReID) refers to the task of retrieving a given probe pedestrian image from a large-scale gallery collected by multiple non-overlapping cameras, which belongs to a specific task of image retrieval. With the development of deep learning, the performance of person ReID has been significantly improved. However, in practical applications, person ReID usually suffers from the problem of occlusion (such as background occlusion, pedestrian occlusion). The occluded image not only loses partial target information, but also introduces additional interference, which makes the deep neural network difficult to learn robust feature representations and seriously degrades the performance of person ReID. Recently, generative adversarial network (GAN) has shown the powerful image generation ability on various computer vision tasks. Inspried by GAN, a person ReID method is proposedunder occlusion based on multi-scale GAN. Firstly, the paired occluded images and unoccluded images are usedto train a multi-scale generator and a discriminator. The multi-scale generator can restore the lost information for randomly occluded areas and generate high-quality reconstructed images; while the discriminator can distinguish whether the input image is a real image or a generated image. Then, the trained multi-scale generator is usedto generate the de-occluded images. Adding these de-occluded images to the original training image set can increase the diversity of training samples. Finally, a classification network is trainedbased on the augmented training image set, which effectively improves the generalization capability of the trained model on the testing image set. Experimental results on several challenging person ReID datasets demonstrate the effectiveness of theproposed method.