The analysis of ancient painted pigments is an important content of technological archaeology and cultural relics conservation research, which has important academic value and practical significance for exploring the development of ancient pigment technology and scientific conservation of cultural murals. Most of the traditional pigment identification algorithms are aimed at the pure pigments on the surface of painted cultural murals, whose identification accuracy is relatively poor for the mixed pigments on the surface of cultural relics. At the same time, chemical analysis methods usually require sampling of the surface of murals, which can easily cause damage. Hyperspectral technology is an emerging technology that has developed rapidly in recent years and has wide application in material identification. A method of mixed pigment identification based on hyperspectral intervals is proposed. Firstly, the first-order derivative of the reflectance spectrum of the unknown pigment is calculated, and the characteristic subinterval range is determined according to the “bump” of the first-order derivative curve of the unknown pigment. If the number of subintervals exceeds 2, only the two most obvious “raised” subintervals will be retained. Secondly, the reflectance curves of the unknown pigment and the standard pigment are transformed to the absorption-scattering ratio (K/S) using the KM model, which aligns more with the linear mixing characteristics. These K/S curves are normalized to［0, 1］ within the characteristic subinterval range. We calculate the similarity between the K/S curves of the unknown pigment and the standard pigments using the Spectral Angle Cosine combined with the Normalized Euclidean Distance. The top three results with the highest similarity for each characteristic subinterval are selected. Finally, one standard pigment K/S curve is removed from the identification results for each characteristic subinterval. Different standard pigment K/S curves from different characteristic subintervals are combined individually to generate the collection of subinterval identification results. They are combined with the abundance matrix obtained from the Dirichlet distribution function to generate 1 000 simulated mixed K/S curves. The similarity between the simulated mixed K/S spectra and the unknown pigment K/S curves is calculated again. We select one standard pigment with the highest similarity value in each collection. The similarity is compared again, and only the pigment with the highest value is identified as the final pigment for the unknown pigment spectra. The pure pigment samples were made by selecting Azurite, Malachite, Orpiment, and Cinnabar pigments. Six sets of mixed pigment samples were made by mixing pure pigments one by one. After the samples were drawn, the imaging data was collected by a hyperspectral imager. The feature subintervals are extracted, and the pigments are recognized by the proposed method after pre-processing. All the results were identified correctly except the Cinnabar in the mixed samples of Malachite and Cinnabar. The overall recognition rate for the mixed pigment samples is 83.3%. The results show that this method can identify mixed pigments and has practical significance for analyzing cultural relics pigments.