To address the existing issues in image restoration methods when repairing damaged murals, such as insufficient attention to contextual information, image blurring and texture inconsistency, this paper proposes a learning based texture coherence and continuity for mural inpainting model(LTCC-MIM). Based on the theory of masked autoencoders, this paper first employs an image inpainting network with a Transformer architecture to integrate local features and global contextual information of the image, thereby enhancing the understanding of interrelationships among various parts of the image as well as its overall structure. Next, a contextual feature enhancement module (CFEM) is designed to achieve regional joint learning and multi-scale feature aggregation, strengthening local texture continuity and generating fine image details to alleviate image blurring. Structural similarity metrics are incorporated as constraints to prioritize mural-specific texture patterns, thereby harmonizing inconsistencies and optimizing restoration quality. Experimental results on digital restoration of real murals demonstrate that the proposed method outperforms comparative methods in both subjective and objective evaluations. Specifically, it achieves an average improvement of 2~8 dB in peak signal-to-noise ratio (PSNR), 2%~10% in structural similarity index (SSIM) and a reduction of 2%~9% in learned perceptual image patch similarity (LPIPS). Additionally, the fr-chet inception distance (FID) score decreased by an average of 1~5 units.