Abstract: Painting is an important form of culture and art. For thousands of years, a large number of paintings have been produced in ancient China. And paintings contain rich cultural, artistic, scientific, and historical values. But due to natural disasters (earthquake) and various reasons such as natural weathering and more and more human economic activities, some paintings are more or less damaged or missing in large pieces, which seriously affects the appreciation, cultural creativity, cultural communication and other activities based on these paintings. Compared with natural images, ancient painting images usually have high self-similarity, obvious style characteristics, rich cultural connotation and delicate texture. Although impressive progress has been made in the inpainting technology of natural images, these methods cannot be directly applied to the inpainting of ancient Chinese paintings. Combined with the characteristics of ancient Chinese paintings, we design the algorithm and model structure and proposes a Chinese ancient painting inpainting algorithm based on a multi-channel encoder and dual attention module. The goal is to automatically repair the damaged ancient paintings. In order to better repair the ancient paintings from multiple scales, we use a multi-channel encoder to learn the semantic features of ancient paintings at different scales and repair ancient paintings through the learned macro, meso, and micro semantic features, which solves the difficult problem of rich and delicate texture repair of ancient paintings. In order to better learn the global semantic features of ancient paintings and conduct the harmony and consistency of the repaired ancient paintings, we use the dual attention module to learn the global semantic features of ancient paintings from two aspects: style and content. In order to verify the advanced nature of the algorithm, an ancient painting data set is produced. Experiments on this dataset prove that the algorithm proposed in this paper has better repair quality than the SOTA algorithms.