Aiming at the defects of existing algorithms such as structural confusion and texture blurring when repairing murals with complex patterns, a dual-generation adversarial network model incorporating structural and textural feature guidance is proposed. Firstly, U-Net is introduced into the dual-generation network, and the texture and structure information extracted by using the direction and channel dual-attention mechanism guides the structure and texture decoders to complete the feature reconstruction of the structure and texture, respectively, and combines with the null residual block and the jump connection to achieve the extraction of multi-scale feature fusion. Secondly, the feature maps output from the two branches are deeply fused by the dual gated feature fusion module to complete the feature information interaction. Finally, the defect repair is completed through the joint dual-discriminator confrontation, enhancing the detail richness and global consistency of the mural restoration effect.The experiments use self-made dataset of non-national treasure real murals somewhere in Wutai Mountain for training and testing, and verified by comparison experiments and ablation experiments, this paper achieves an average improvement of 4.24 dB in the peak signal-to-noise ratio metric, and improves an average of 3.6% in structural similarity index. The experiments show that the method can effectively repair the damaged murals, so that they present better structural and textural information, and the visual effect is clearer and more natural.