Since the proposal of generative adversarial networks, GAN has become a research hotspot of artificial intelligence. GAN adopts the method of the zero-sum game between two people, which consists of a generator and a discriminator. The generator is responsible for generating the sample distribution, and the discriminator is responsible for determining whether the input is a real sample or a generated sample. The generator and discriminator constantly interact and optimize to achieve the optimal effect. The model of GAN is undoubtedly very novel, but there are also many shortcomings, such as the problem of gradient disappearance, collapse mode, and so on. With the deepening of research, GAN has been continuously optimized and expanded, and the derivative models of GAN have emerged in endlessly. GAN has been optimized and improved. Also, GAN can be applied in different fields, though it is mainly used in the field of computer image and vision. It has outstanding effects in the field of image. It can generate high-resolution realistic images, repair images, transfer styles, and generate video and prediction. GAN can also generate text, to do some work, such as dialogue generation, machine translation, voice generation, and so on. GAN is also involved in other fields, such as generating music and decoding codes. However, the application effect of GAN in other fields is not significant. Therefore, how to improve its application effect is worthy of further study, which will make the generation of confrontation networks shine in artificial intelligence.