To overcome the problem of a single spectral feature not finely characterizing the type and degree of weathering in the complex weathering area on a grotto surface, this paper proposes an intelligent quantitative evaluation method for grotto surface weathering based on spectral analysis and colorimetric theory. First, we reconstruct the reflection spectrum of the multispectral image of the grotto surface, calculate the color difference between each pixel and reference point, and use the principal component analysis method to extract the principal component features from the multispectral image data. Then, we fuse the spectral color difference and principal component features of the weathering on the grotto surface to characterize different types and degrees of weathering. Finally, a random forest classifier is used to intelligently evaluate the weathering degree of each pixel in the grotto surface multispectral image. Experiments show that the method of fusing spectral chromatic aberration and principal component features performs better compared with a single spectral feature in characterizing different types and degrees of weathering in complex weathering areas. The evaluation accuracy of the overall grotto surface weathering degree is 99.86%, with a Kappa coefficient of 0.99. The proposed method can effectively realize a refined characterization of complex weathered areas.