Aiming at the shortcomings of the polynomial regression color correction method, an adaptive weighted root polynomial regression algorithm is proposed. In the process of polynomial regression color correction, it is necessary to manually calibrate the position of the color block of the color card, which is complicated and prone to human error. In view of the problem that the high-order term of the polynomial will amplify the noise and is not robust to the noise, the algorithm in this paper will adaptively adjust the weight matrix to reduce the influence of singular values on the fitting performance, and then calculate another gain coefficient matrix from the color difference value, thereby improving the correction accuracy. It has been verified by experiments that the algorithm in this paper has a great improvement in the CIELab color difference value and PSNR compared with the traditional polynomial regression method. Among them, the average CIELab chromatic aberration value of the traditional polynomial regression method is as high as 6.5, which is greatly affected by the environment. However, the chromatic aberration value of the proposed algorithm can be stabilized below 3.2 after correcting images in different environments.