Five kinds of typical Yang-boosting Chinese herbal medicine are identified and classified based on near infrared spectroscopy (NIR) and latent semantic analysis (LSA) methods. Latent semantic analysis is used for characteristic extraction and classification of preprocessed spectral data of 5 kinds of Yang-boosting Chinese herbal medicine. The spectral characteristic data, after spectral pretreating and characteristic extraction by principal component analysis (PCA), are respectively subjected into the K-nearest neighbor (KNN), BP-artifical neural networks (BP-ANN) and least squares support vector machine (LS-SVM) classification models whose results then are compared with the result of latent semantic analysis model. In the characteristic wavenumber range of 4119.20~9881.46 cm-1, spectral data pretreated by multiplicative scatter correction (MSC) are substituted to LSA classification model when spacing dimension of underlying language is 3, and accuracy rates of both training set and test set are 100%. The results show that latent semantic analysis, which has a good application prospect and practical significance, can be used as a new method for spectral information extraction and classification in the near-infrared spectroscopy identification of Yang-boosting Chinese herbal medicine.