Color constancy is an important prerequisite for computer vision tasks such as object detection, three-dimensional object reconstruction, and automatic driving. In order to make full use of the feature information of different scales in the image to estimate the light source, a progressive multi-scale feature cascade fusion color constancy algorithm is proposed. The feature information in the image is extracted from different scales by three convolution network branches to fuse and get more abundant feature information. By cascading the shallow edge information and the deep fine-grained feature information in the image, the accuracy of the color constancy algorithm is improved. The progressive network structure improves the robustness of the algorithm for the light source estimation in extreme scenes by weighted cumulative angle error loss function. Experimental results on the reprocessed ColorChecker and NUS-8 datasets show that the proposed algorithm outperforms the current color constancy algorithm in terms of various evaluation indexes, and can be applied to other computer vision tasks requiring color constancy preprocessing.