To address the problem of spectral information shift caused by uneven illumination during multispectral imaging of painted cultural relics in complex environments, a region-adaptive multispectral image correction model constrained by a priori spectral features is proposed. First, multiscale guided filtering is used to estimate the illumination component, effectively addressing issues of edge blurring and halo artifacts. The image is then divided into regions, and an adaptive gamma correction function is developed based on the illumination characteristics of these regions to process the illumination component, and balance bright and dark areas. This process is combined with contrast-limited adaptive histogram equalization to enhance local contrast, and the result is fused with the original illumination component. Finally, spectral correlation and consistency across multiple imaging channels are constrained by introducing spectral structure and gradient loss functions as a priori features, characterizing the spatial structure and trend variations of the multispectral images. Experimental results show that compared with other algorithms, the regional adaptive illumination correction algorithm constrained by a priori features reduces the root-mean-square error (RMSE) by an average of 26.96% and increases the spectral correlation measure (SCM) by 21.63% for simulated murals. For real murals, the RMSE is reduced by 11.12% on average, while the SCM is improved by 12.65%, significantly enhancing pigment classification accuracy.