In order to solve the problems of difficulty in obtaining paired data sets and poor quality of enhanced images in the process of low-light image enhancement, the implementation of unpaired low-light image enhancement was studied by improving the cycle generative adversarial network model. In the generator part, a U-NET model integrated with the Vision Transformer structure was used to replace the original generator model, so as to improve the cycle consistency and content retention of image transformation, and effectively deal with the problem of long-distance spatial correlation commonly existing in image research. In the discriminator part, PatchGAN was selected to replace the traditional discriminator according to the characteristics of image research to improve the discrimination ability of image details. At the same time, the identity consistency loss function was introduced to improve the image quality. The results show that compared with the traditional method, the improved model in this paper has better subjective visual effect, and also has a corresponding improvement in the objective evaluation index. It can be seen that the improved model in this paper is effective.