Recently, most Thangkas are preserved in the form of digital images. These Thangkas have lost detail and reduced resolution due to oversights in preservation techniques. This paper proposes to apply super-resolution to low-quality Thangkas to retain more semantic information while maintaining image fidelity. Specifically, a high-resolution Thangka dataset is constructed for model training. Subsequently, the semantic information of images are injected in hidden feature based on SinSR, which extracts semantic information from low-resolution images during the original downsampling process, then integrates the semantic masks into the residual blocks of the Unet framework. At the same time, we conducted an in-depth analysis of multiple fusion locations and methods, selecting the most suitable solution. Experiments on the Thangka dataset illustrate that, compared with other models, our method not only strengthens semantic fusion but also preserves finer local details, yielding an 8% improvement in CLIPIQA scores over the original model and a 5% gain in MANIQA scores.