The current mainstream image colorization methods include traditional algorithms and deep learning methods. With the development of deep learning models, the grayscale image colorization method based on deep learning can bring better coloring effects, but there is still a loss of details and dull coloring. In order to solve these problems, in this study, the CycleGAN model is applied to the colorization of non-single-category grayscale images, so as to achieve realistic coloring effects on pictures of animals, plants, landscapes, etc. The activation function of the CycleGAN model is improved in terms of model structure, and the PReLU activation function is used in the generator to make the model easier to be trained. This study also uses PatchGAN in the discriminator to improve color details at high resolution in the image. After training on five popular categories of images from the ImageNet dataset, the model’s colorization effect on animals, plants, and landscapes is realistic. In the image evaluation index, the model is 0.603 dB higher than GAN in PSNR, which indicates an improvement of about 2.1%, and it is significantly higher than other models in SSIM, with an improvement of 5.1% in effect. From the perspective of visual perception, the pictures colored by CycleGAN have higher saturation and visual authenticity than models such as VGG and GAN. As a result, the proposed model not only solves the problem of dull coloring but also makes it easier to restore the color details in the picture and avoid the loss of details.