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Subsequently, a large number of theoretical and technical research results of generative countermeasure networks came out one after another, and some of them played a milestone role in promoting the overall research progress of generative countermeasure networks. The verification model of the original GANs is realized by multi-layer perceptron MLP, and the generation quality is poor [1]. The literature proposes DCGANs, and the generator and discriminator are realized by deep convolution network respectively, so as to ensure the engineering implementation of GANs in the field of graphics generation. In order to improve the training stability of GANs and make the output data have a certain controllable directivity, the literature proposes the conditional generation confrontation model CGANs [21]. On this basis, the literature adds a supervised learning classification task to GANs to form ACGANs to improve the generation quality of the model. In order to further explore the training stability of GANs, some targeted training skills are added in the training process of GANs to form improved GANs. The literature improves the loss function of GANs from the mathematical principle, so that GANs can better narrow the distribution of generated data and training set data [22]. This research work has played an important role in the development of GANs technology, and also made a series of application achievements in many task fields, especially in the direction of graphics and images. At present, there are many research works on GANs with wide coverage. This paper investigates and classifies the existing achievements from the key technical level and application level. It should be noted that the same work may contain improvements in multiple directions [23]. 2b1af7f3a8