Panax notoginseng is a traditional Chinese medical herb with high medicinal value. Nowadays, adulteration is common in the Chinese medicine market, and many unscrupulous traders sell rootlet or rhizome powder as the main root powder, which seriously damages the interests of consumers. Therefore, this study aims to rapidly and non-destructively identify Panax notoginseng powder of different parts by applying a hyperspectral imaging techniques combined with multivariate analysis methods. The hyperspectral images of Panax notoginseng rhizome, fibrous root and main root powder were collected by the hyperspectral imaging system in the spectral range of 400~1 000 nm （a total of 300 samples）. Savitzky-Golay（SG）smoothing combined with Standard Normalized Variate （SNV） was applied to eliminate the noise in spectral data and reduce the spectral difference caused by scattering. In order to remove the overlapping and redundant information in spectral variables, a Binary Competitive Adaptive Reweighted Sampling （BCARS） algorithm that considers the interaction effect among variables proposed in this paper was used to select the feature wavelengths. At the same time, the Competitive Adaptive Reweighted Sampling （CARS） algorithm was also used. Based on the full spectrum, CARS and BCARS feature wavelengths, Support Vector Machine （SVM） and eXtreme Gradient Boosting （XGBoost） classification models were established, respectively. The results showed that the BCARS-XGBoost model had the best performance, with classification accuracies of 100% and 99.33% for the training and prediction sets, respectively. In addition, fewer feature wavelengths were selected by BCARS, which is conducive to developing a multi-spectral system and portable detector. Therefore, it is feasible to identify Panax notoginseng powder of different parts by applying a hyperspectral imaging technique combined with the BCARS-XGBoost model.