In the process of image restoration, in order to solve the problems of incomplete separation of color and texture optical properties and single dictionary design in image inpainting of sparse representation, which leads to the structural incoherence and blurring effect of mural image inpainting results, a mural inpainting method based on robust principal component analysis (RPCA) decomposition of block nuclear norm and entropy weighted clustering sparse representation was proposed. First, the proposed RPCA image decomposition algorithm based on block nuclear norm was used to decompose the mural image into a structure layer and a texture layer, and the block nuclear norm was used to perform texture correction operations, which could overcome the problem of incomplete separation of structure and texture by RPCA. Then, the entropy weighted k-means method was proposed to cluster the structure layer image and construct sparse sub-cluster dictionaries, and the reconstruction of the structure layer image was completed by sparse value decomposition and split Bregman iterative optimization method. Finally, the image of texture layer was repaired by using the bicubic interpolation algorithm, and the repaired structure layer and texture layer were fused to complete the repair of the damaged murals. Experimental results of digital restoration on the real Dunhuang murals show that the proposed method could effectively protect the edges, textures, and other important features in the mural image. In terms of visual quality and quantitative evaluation such as peak signal-to-noise ratio (PSNR), the proposed method had better performance than the comparison algorithms, and the restoration efficiency was higher.