Due to the defects of imaging equipment, it is easy to cause the shift of the imaging color and affect the downstream tasks of the image algorithm. Therefore, the color constancy algorithm is required to correct the image color so that the image color is consistent with the color seen by the human naked eye. The effect of the traditional color constancy algorithm depends on specific light source environments. In order to improve the application range and utilization efficiency of the color constancy algorithm, a color constancy calculation model based on the SqueezeNet framework is proposed, which senses the image light source through the convolutional image network. In addition, the attention mechanism and residual connection are introduced to improve the network's understanding of images and computing performance. The network predicts the illumination color of each area of the input image at the same time and then gathers them by designing three different pooling methods to output the global estimated light source of the image, and it finally uses the estimated light source to correct the image. Experimental results show that the proposed light source estimation algorithm can effectively estimate the illumination color of the image and correct the image color.