<p>Chinese ancient paintings have suffered from human damage or accidental fires, resulting in large-area damage to the paintings. However, the natural image restoration technology is not applicable to ancient paintings. Therefore, a large-area damage inpainting model is proposed for ancient paintings based on Spatial Fourier Convolution. Firstly, Spatial Fourier Convolution is constructed for large-area damage inpainting. In Spatial Fourier Convolution, Spatial Mixed Attention (SMA) is proposed to alleviate artifacts and unreasonable color filling. In addition, Multi-Scale Dilated Attention (MSDA) is introduced in spatial Fourier convolution to effectively capture the multi-scale information. Secondly, the Region Normalization (RN) is introduced to eliminate the artifacts produced due to the loss of feature information in the broken area. Then, in order to address the problem of the loss of line structure information, edge loss is incorporated to retain the original structure information, so as to accurately reconstruct the contours and details of the ancient paintings. Lastly, a large-area damage dataset is created for ancient paintings, and experiments are conducted to evaluate the performance of the proposed model. Qualitative and quantitative experiments show that, compared with the latest inpainting models, the proposed model significantly reduces loss of details, blurred textures, missing lines, and unnatural color reconstruction. Therefore, it is capable of inpainting the large-area damage of ancient paintings.</p>