The current generative adversarial network (GAN) is limited in the application of data augmentation in object recognition. The training of the GAN is unstable, and the generated image quality is poor. Methods such as progressive growing of GANs and multi-scale gradient GAN solve these problems. The packed GAN (PacGAN) solves the problem of mode collapse during training. However, these methods can generate only one type of image at a time, and the training time is long. To solve the above problems, this paper proposes the multi-class GAN (Mc-GAN). It uses an augmented discriminator to train multiple generators at the same time. Through iterative training, the discriminator can accurately judge the output of each generator and guide it to generate the corresponding image. This paper analyzes the optimization process of the objective function of Mc-GAN. Experiments show that the method can generate high-quality images and reduce training time, and it can be used for data augmentation in object recognition. It effectively improves the practicality of GAN.