The invention relates to a generative adversarial network method fusing a self-attention mechanism, belongs to the field of computer vision, and particularly relates to a generative adversarial network for image generation. The generation of the image is an important challenge in the field of computer vision, and if a large number of high-quality image samples can be generated, the artificial intelligence field can be more rapidly developed in the era depending on the big data background. Therefore, the invention provides the generative adversarial network fusing the self-attention mechanism,the network can generate the high-quality images, and meanwhile, the images have higher diversity. Specifically, the generative adversarial network uses a Waserstein distance to measure an evaluationcriterion of the generator and discriminator distribution, and a loss function is correspondingly improved; meanwhile, the self-attention mechanism is introduced into the neural network architecture corresponding to the generator and the discriminator, so that the relevance between the local pixel regions of the generated image is improved, and the quality of the generated image is improved.