Color constancy is an important prerequisite for visual tasks such as recognition, segmentation, and three-dimensional object reconstruction. We proposed a multi-channel feature-confidence-weighted network to enable the computer vision systems to perceive color constancy. As a result, the network could fully extract the features in the images while reducing the number of network layers and model parameters. The multi-channel confidence-weighted method employed the features in each channel that could provide more information for light source estimation to accurately estimate the light source in the global scene. Experimental results on the reprocessed ColorChecker and NUS-8 datasets show that the proposed algorithm, which weights the confidence of features in multiple channels, outperforms its counterparts in terms of all evaluation indexes and thus has higher accuracy and robustness. As such, this algorithm can be applied to the tasks of computer vision requiring color correction.