The existing deep learning methods don’t make full use of the prior information such as semantic and texture information in intact area of mural restoration， which results in poor restoration results， so a mural restoration algorithm based on semantic prior and texture enhancement guidance is proposed. Firstly， a semantic prior learning module is designed， which maps the original mural semantic features to a semantic prior learner through pixel folding operations. The original semantic features are used to guide the repair of incomplete features， gradually reducing the difference between damaged and original semantic features. Then， a texture enhancement module is designed， which enhances texture details by fusing contextual information modules and fusing them to complete the restoration of mural texture features. Finally， an aggregate bootstrap module is designed， which integrates the semantic prior repair and texture enhancement results， decodes them to the original resolution， and completes the repair of damaged murals through adversarial games with Markov discriminators. The digital classification restoration experiment of Dunhuang murals shows that the proposed method outperforms the comparative algorithm in both subjective and objective evaluations， achieving better restoration results.