Linear feature is an important element in murals. However, natural or human factors tend to make it difficult for human eyes to distinguish some blurred lines of the murals. Therefore, a linear feature enhancement method using hyperspectral image block principal component analysis (PCA) and image unmixing was proposed. Firstly, the support vector machine (SVM) was employed to classify the hyperspectral composite image of the mural, the result of which could help produce the mural label data. In doing so, the block data of the homogeneous area of the hyperspectral image could be acquired. Secondly, vertex component analysis (VCA) was performed on each segmented image to obtain a candidate endmember set. The final endmember set was determined by constructing a projection matrix and merging similar endmembers. Then, the non-negative least squares unmixing was used to obtain the line abundance map. Finally, the first principal component image of the block principal component analysis was normalized, and band calculation was performed with the line abundance map to obtain the linear feature enhanced image. They were fused with the true color composite image to obtain the linear feature fusion image. Taking some hyperspectral images of murals in Qutan Temple, Qinghai Province, China as an example, the results show that the algorithm can enhance the linear features in the murals, which is superior to the PCA enhancement method.