Hyperspectral remote sensing has been widely applied in geological research due to its rich multi-band spectral information. Most studies mainly focus on the identification of soil components and clay minerals, with relatively fewer studies on carbonate rocks, so this paper proposes a decision tree model to achieve precise classification of carbonate rocks based on hyperspectral data. A continuum-removed method is used to preprocess the data, and then combines spectral knowledge and machine learning to extract features. Specifically, the study determines spectral intervals closely related to carbonate rocks through spectral knowledge and extracts key waveform features from the spectral curves. Subsequently, the study uses the random forest algorithm to select features with discriminative capabilities, determines the optimal classification discriminant through threshold analysis, and builds a decision tree model. Finally, the model performance is evaluated using a confusion matrix, and the classification accuracy is compared with other five models. Results show that the decision tree model constructed based on the order of the lowest point wavelength of the absorption valley, the right shoulder wavelength of the absorption band , and the absorption bandwidth exhibited the highest classification accuracy, with an accuracy rate of 95.57%.