This paper proposes a new no-reference quality assessment method for inpainting Thangka image based on multiple features, and combining the structural and color characteristic of Thangka images to solve the problem that a single feature is confined to reflects the difference in the effect of restoration methods. The proposed algorithm not only uses the rich texture of Thangka images but also selects Gaussian difference operator to extract the line drawing of target image, and combining symmetry characteristic of Thangka image to obtain structural features. Secondly, the color features of Thangka images are extracted according to the difference of color entropy between each superpixels after simple linear iterative cluster segmentation. Finally, considering that the multi-scale features are more consistent with the human visual characteristics, the decomposed image features are input into the adaptive neural network for training, and the objective evaluation score of image quality is predicted. The experimental results show that this method can obtain the scores which is consistent with the subjective evaluation by utilizing the structure and color characteristics of Thangka images, and its Spearman correlation coefficient and Pearson correlation coefficient are both above 0.94.