The content of ancient calligraphy artifacts contains crucial information documenting the social civilization of ancient China. Obtaining and identifying hidden inscriptions within the paper surface is important for historical background research on cultural relics and the collection of humanistic classics. However, traditional methods for identifying hidden inscriptions in calligraphy artifacts rely heavily on manual interpretation, which requires extensive expertise from researchers and results in a time-consuming analysis that may inadvertently cause secondary damage to the artifacts. To address this challenge, hyperspectral remote sensing, with its non-contact and efficient characteristics, captures the spatial properties of paper-based artifacts and acquires rich spectral information. This enables the digitization and storage of calligraphy artifacts. Initially, the Minimum Noise Fraction (MNF) transformation technique was utilized to reveal latent blurry information in calligraphy artifacts. Subsequently, a spectral transformation method based on Linear Difference Enhancement (LDE) was developed to identify these details further, and statistical analysis was conducted on the spectral parameters before and after transformation and the component images extracted by MNF. By utilizing entropy evaluation, we obtained the most information-rich spectral image, ultimately enabling the successful identification of the hidden inscriptions within the calligraphy artifact. The research results demonstrate the following: (1) The MNF transformation of the hyperspectral image of the calligraphy artifact reveals the blurred patterns hidden in the inscriptions. These patterns exhibit similarities in spectral morphology with other content elements of the calligraphy artifact but with differences in reflectivity. (2) The LDE algorithm effectively amplifies the relative differences between hidden information within the calligraphy artifact and the spectral bands of the inscriptions. LDE significantly enhances most spectral features of the hidden inscriptions after enhancement. (3) Following LDE processing, the calligraphy artifact data shows improved image entropy values in the wavelength range, spectral characteristics, and MNF sub-components. Particularly, the entropy value of the spectral variance (SV) image after LDE processing reaches 6.74 bits. (4) After LDE processing, the SV image of the calligraphy artifact successfully identifies the hidden inscriptions on the paper that do not belong to the “Heart Sutra” content. This finding proves that the paper used for this calligraphy artifact is dedicated to sutra writing. This discovery effectively reveals and identifies hidden inscriptions behind Emperor Qianlong’s ink treasure, enriching the historical and humanistic background of the artifact. It also provides scientific, theoretical, and technical support for future studies on extracting hidden inscription information in ancient calligraphy artifacts.