Affected by preservation conditions, foxing will form on the surface of many paper cultural relics. If effective monitoring and scientific judgment are not carried out, the safety of paper cultural relics will be further affected. For the detection of foxing disease on paper cultural relics, there are problems such as hysteresis and subjectivity. It is difficult to identify the area covered by ink, paint and seals in the painting and calligraphy collection. Therefore, the concept of preventive protection based on cultural relics needs to be developed urgently. Non-destructive testing technology for efficient and accurate identification of foxing. The visible-near-infrared hyperspectral image combines spectrum and image, contains rich spatial information and spectral information, and can achieve lossless batch collection of sample spectral information on the flat. This research proposes a rapid identification method based on hyperspectral imaging technology to detect foxing on paper cultural relics. Obtain hyperspectral images of simulating paper cultural relics at the 360~970 nm. Because the 360~450 nm image is much affected by noise, we choose to exclude this part of the spectral data; select the region of interest, obtain the corresponding average spectral reflectivity, and compare the healthy area with that. In the area of foxing infection, it is found that there is a difference in the spectral curves of the two; near 450~600 nm, the spectral reflectivity of the affected area of foxing is higher than that of the healthy area, and the peak shape appears near 600 nm; and in the range of 600~900 nm, The spectrum of the infected area and the healthy area tends to be stable, and the difference between the two gradually decreases. Select the feature information extracted from the image corresponding to the feature wavelength to build an image recognition model, using band math to observe the image characteristics of foxing, the size and distribution of the foxing can be displayed, but the overlapping parts with the seal and ink, the foxing are covered by the seal and ink, which is difficult to identify; use the minimum noise fraction, although different parts are overlapping, it can find hidden foxing that is difficult to identify with the naked eye; 180 pieces of hyperspectral data (450~970 nm) establish a foxing discrimination model, randomly divided into 120 pieces of data as the training set, and 60 pieces of data as the test set, K-nearest neighbor method and BP neural network are used to establish a paper cultural relics foxing spectrum discrimination model. In general, the two methods have distinguished rates of 73.3% and 85% respectively; Comparing with the K-nearest neighbor model, the BP neural network has a higher overall discrimination rate and a better recognition effect. The results show that hyperspectral imaging can efficiently and accurately identify the foxing of paper cultural relics, provide reliable technical means for the follow-up research on the distribution and development of foxing, and provide guidance for the preservation of cultural relics in the museum.