To address the issues of spatial feature redundancy and insufficient feature interaction in the inpainting process of Dunhuang murals,a Dunhuang mural inpainting model that combining multi-scale features and spatial reconstruction is proposed.This method initially employs a spatial reconstruction unit (SRU) to enhance feature learning capacity through feature separation and reconstruction operations.Subsequently,an aggregated multi-scale context module (AMC),which combines multi-scale fusion and gated residual connections,is designed to update the features in missing regions.Finally,a 3D-configured global attention mechanism (GMA) is integrated into the structure-texture feature fusion network to reinforce the synergy and interaction between texture and structural features.Experimental results on the Dunhuang mural dataset demonstrate that the proposed method effectively inpaints damaged Dunhuang murals, achieving well-preserved structural integrity and detailed features.It also exhibits superior performance in inpainting large-area damage and reconstructing complex textures.