Red mineral pigments have been widely used in ancient paintings and ancient buildings by artists. It is of great significance to correctly identify different kinds of red pigments to monitor and restore cultural relics. Traditional pigment identification mainly relies on chemical analysis, which has a slow identification speed and small identification range and will cause permanent damage to cultural relics by sampling operation. Nondestructive identification of pigments by hyperspectral technology can solve these problems well. In this study, ten red mineral pigments, namely Cinnabar Ⅰ, Rouge, Vermilion, Zhi Xi, Cinnabar Ⅱ, Ochre, Ochre powder, Iron oxide red, Terra Rossa and Western red, were selected as the objects of study. The original digital (DN) images of the hyperspectral data of the 10 red pigments in the band of 350~2 500 nm were obtained by using a ground object spectrometer in an optical darkroom, and the reflectance data and spectral curves which can be directly used for spectral analysis were obtained by reflectance correction. Based on the characteristics of different spectral curves of 10 red pigments, the spectral characteristic bands of the distinguished pigments, namely target pigments, were obtained by two-step screening. By taking the extreme point of the spectral curve of the target pigment as the characteristic band, the primary spectral characteristic band of the target pigment can be screened. The reflectance of the other 9 pigments corresponding to the primary spectral characteristic band was differentiated from that of the target pigment in this band. As for the difference, the sum of squares was calculated after screening outliers. Different bands correspond to different sums of squares of differences, and the first 4 bands with larger sums of squares of differences were selected as the optimized spectral characteristic bands. Based on the normalized spectral index model formula (NDSI=(R_a-R_b)/(R_a+R_b), R_a and R_b are the reflectance values of target pigments at spectral characteristic bands a and b respectively), the normalized spectral index was constructed for 10 red pigments. Compare and analyze the spectral index of the target pigment and the other 9 kinds of red pigments in the same spectral characteristic band, and the difference between the spectral index of the target pigment and the spectral index of the other pigments was calculated to evaluate the different effect. For the four optimized spectral characteristic bands, six normalized spectral indexes could be constructed, and the normalized spectral index with the minimum and maximum distinctness was selected as the characteristic spectral index of the target pigment. The research result shows that the minimum discrimination between each target pigment and other pigments remains above 0.7 when the target pigments are distinguished by their respective characteristic spectral indices (more than 0.5 can be considered to be clearly distinguishable), indicating that the above method can accurately distinguish each red pigment, which is of practical significance for the rapid and accurate identification of cultural relic pigments.