Existing artificial local measurement methods are often used to evaluate the degree of surface weathering of grottoes. However, such methods are inefficient and the evaluation results are easily affected by subjective factors. In this paper, an intelligent quantitative evaluation method for grotto surface weathering based on multispectral imaging and random forest algorithm was proposed. Multispectral imaging was used to extract the the surface spectral information of grotto to characterize the type and degree of weathering. The multispectral feature data were reorganized and normalized to establish training, testing, and prediction samples. Based on the theory of minimum relative entropy, a loss function was designed to train a random forest algorithm model, and the spectral characteristics of samples with different weathering types and degrees were extracted. The weathering degree of each pixel in multispectral images of grottoes was predicted and evaluated using a classification model with feature perception ability after training. The confounding matrix and Kappa coefficient were used to evaluate the accuracy of the results. The proposed method was verified taking the Wanfo temple grottoes, Qingliang mountain, Yan'an city, Shaanxi Province as an example. Results show that the target grottoes’ strong salting-out weathering surface area ratio was 5.15%, weak salting-out weathering area ratio was 27.88%, slight salting-out weathering area ratio was 27.39%, and strong dust weathering zone ratio was 39.58%. The evaluation results were basically in accord with actual weathering conditions. Accuracy was 98.49% and the Kappa coefficient was 0.98. The proposed method can realize pixel-level refined evaluation.