The conventional cycle generative adversarial network （CycleGAN）， as an image style transfer model that does not require paired datasets， in the task of low-light domain image enhancement， there are problems such as the loss of details in the generated images， color distortion， and poor adaptability in complex scenarios. Based on this， this paper proposes an improved CycleGAN model which aims to enhance the effect of low-light image enhancement. First， in the design of the generator， a two-stage color correction module is integrated during the upsampling phase to alleviate the problems of color distortion and quality degradation in low-light environments. Second， the channel-spatial hybrid attention mechanism is embedded in the skip connection layer to achieve the adaptive strengthening of key information during the feature fusion process. Then， in the design of the discriminator， a global-local discrimination mode is adopted， enabling it to take into account the discrimination ability of both global information and local details. Finally， in the design of the loss function， perceptual style loss and content loss are introduced on the basis of adversarial loss to further improve the structural fidelity and visual naturalness of the generated images. Through the subjective and objective experimental evaluations and comparison with various representative image enhancement models， results show that this model has a good enhancement effect on low-light images. It can effectively enhance the overall brightness and local details of images without causing color distortion， thus improving the quality of the generated images.