Hyperspectral remote sensing technology can show the spectral characteristics of rocks and minerals in more detail, which provides a powerful means for hyperspectral rock and mineral identification. The traditional hyperspectral rock and mineral identification model based on specific absorption characteristic band depends on high a priori knowledge and is difficult to meet the requirements of distinguishing different types of rocks. Therefore, exploring and establishing an accurate and efficient hyperspectral rock automatic identification model is of great significance. Three typical sedimentary rocks (21 mudstone, sandstone and limestone) were collected as target samples in the Lufeng Dinosaur Valley area. The hyperspectral data of sedimentary rock samples in the range of 350~2 500 nm were obtained with the aid of the ASD fieldspec3 ground feature spectrometer. The original spectrums first-order differential and continuous removal transformation were carried out, and the spectral characteristics were analyzed. The continuous projection algorithm (SPA) was used. Competitive adaptive reweighted sampling algorithm (CARS) and iterative retained information variable method (IRIV) select the characteristic wavelengths in the original spectrum and transformed spectrum and then establish support vector machine (SVM) and random forest (RF) recognition models based on the full band and characteristic wavelength data respectively. The results show that the three feature variable selection algorithms have a good dimensionality reduction effect on hyperspectral data, and the number of feature wavelengths selected from the original spectrum and the two transform spectra is between 7~59. It is obtained that the combined continuum removal SPA-SVM model method performs best for identifying three types of target sedimentary rocks, and its recognition accuracy is 0.952 4. At this time, 10 characteristic wavelengths are selected for the input model, which accounts for only 0.5% of the whole band, which greatly reduces the amount of calculation of the model. Two characteristic wavelengths are located in the Fe2+ and Fe3+ absorption bands near 550 nm, Two Fe3+ absorption bands near 900nm and five water molecules and hydroxyl absorption bands near 1 900, and 2 200 nm can better reflect the spectral absorption characteristics caused by the difference of chemical composition of sedimentary rocks. The experimental results show that the automatic recognition of hyperspectral sedimentary rocks using spectral transformation and characteristic variable selection algorithm is feasible and can provide a reference for hyperspectral rock and mineral recognition methods.