To solve the problem that the existing deep learning algorithms do not fully consider the consistency of the information between the damaged area and the intact area when repairing mural images， which leads to boundary effects and texture blur in the repair results， we proposed a progressive mural inpainting algorithm combining feature reasoning and semantic enhancement. Firstly， the progressive structure of the region was designed to realize the progressive contraction of the region to be repaired. Then， the feature reasoning module was used to iteratively fill the feature values of the missing pixels， reduce the reconstruction error of the mural restoration， and enhance the correlation between the damaged area and the intact area of the mural. Finally， the feature maps of each layer were adaptively fused， and the semantic enhancement module was used to transfer the texture details， so as to improve the consistency of the mural completion area and the whole. The digital restoration experiments of Dunhuang murals show that the restored murals by the proposed method have better consistency of texture details， and are superior to the comparison algorithms in subjective and objective evaluation indicators.