The quality of image fusion in complex visible light images is affected by factors such as occlusion and overlap. It is necessary to optimize the design of image fusion quality evaluation. A visible light image fusion quality e- valuation model based on big data analysis is proposed, and a deep stereo matching model for visible light images is es- tablished using the visual feature extraction method between corresponding image blocks, Map the pixel values of ima- ges collected under different lighting intensities to the embedded feature space, finish preprocessing, construct a dy- namic pixel big data matching model for visible light images, achieve dynamic fusion of visible light images through end-to -end disparity fusion estimation, and the superresolution reconstruction method was used to obtain the real paired images, and the similar contents of SR results and LR images were analyzed. The feature level image distribu- tion domain was used to reflect the visible image fusion quality evaluation, and the visible image fusion quality evalua- tion was realized. The simulation results show that the matching performance of visible image fusion using this method is better, the image contrast and saturation are high, and the imaging quality of visible light is improved. The time consuming is 0. 012 s, the average number of iterations is 1. 569, and the mean square error is only 1. 071, and the to- tal error is only 4. 646. The method effectively improves the image fusion quality. Improve the evaluation effect.