Color constancy is an important research direction in computer vision, but most algorithms focus on uniform distribution of single illuminant, and the problem of non-uniform distribution of illuminant has not been well solved. In order to solve this problem, a direct correction method based on generative adversarial network is proposed to transform color constancy into a many-to-one mapping task under the condition of non-uniform distribution of single illuminant. According to the characteristic of color constancy, the image is divided into content code and illuminant code, and the image under the target illuminant is reconstructed by changing the illuminant code to target illuminant code. At the same time, in order to make non-standard illuminant more diversified, the illuminant sampling module is added to help the network learn more abundant illuminant information and realize many-to-one mapping. In order to guide images to be mapped to different illuminants when different illuminant codes are input, the illuminant supervision module is added to distinguish images with different illuminants, so as to help the illuminant conversion module better combine content coding with specific illuminant coding to generate target images and achieve color constancy. At the same time, aiming at the task of this paper, the non-uniformly distributed illuminant is rendered on the existing dataset, and the dataset with non-uniformly distributed single illuminant is constructed. The experimental results show that the proposed method solves the problem of non-uniform distribution of illuminant well, surpasses other algorithms in non-uniform dataset, and the final image is closer to the image under standard illuminant.