In view of the problems that the existing deep learning repair methods are greatly affected by the structure and texture， resulting in structural disorder and texture blur in the repair results， a generative adversarial mural restoration model jointly guided by structure-gated fusion and texture is proposed. Firstly， the generation net-work composed of a structure-guided coding sub-network and texture-guided decoding sub-network is designed， which uses structure information to guide coding， and enhances edge contour information through gated features. Then， the texture guide and orientation attention module are used to extract layered texture features， guide the de-coder to repair， and improve the texture consistency of murals. Finally， skip connection is used to promote the feature complementarity of structure and texture， and the spectral-normalized PatchGAN discriminant model is used to com-plete mural restoration. The results from the digital restoration experiment of real Dunhuang murals show that the subjective and objective evaluation of the proposed method is better than the comparison algorithm， and the restora-tion results are clearer and more natural.