Panax notoginseng powder is the main consumption and commodity form of panax notoginseng. There are shoddy or even adulterated phenomena in the market. As panax notoginseng powder is a powdery material, it is not easy to distinguish with the naked eye. In order to identify the quality grade of panax notoginseng powder, visible near-infrared hyperspectral imaging technology was used to identify the panax notoginseng powder with different quality grades. The taproots of panax notoginseng of 30 heads, 40 heads, 60 heads and 80 heads were ground into powder to prepare samples. The hyperspectral image of 384 samples of four quality grades was acquired by using a visible near-infrared hyperspectral imaging system(400.68~1 001.612 nm). Region of interest (ROI) was extracted from the hyperspectral image, and the average spectral value of samples was calculated. 384 samples of panax notoginseng powder were divided into training sets and test sets in a ratio of 2∶1. The original spectra of panax notoginseng powder were preprocessed using multiplication scatter correction (MSC), Savitzky-Golay (SG) and standard normal variable (SNV), and the support vector machine (SVM) was employed to form the classification models based on MSC, SG and SNV. By comparing the classification accuracy of SVM models based on MSC, SG and SNV, it was found that SNV had the best effect on preprocessing. Iterative reserved information variable (IRIV), variable combined cluster analysis (VCPA) and variable combined cluster analysis and iterative reserved information variable (VCPA-IRIV) were adopted to extract feature wavelengths from the spectra after SNV pretreatment, and the SVM was employed to form the classification models based on feature spectra and original spectra. By comparing the range of feature wavelengths and the classification accuracy of SVM models based on IRIV, VCPA and VCPA-IRIV, it was found that VCPA-IRIV, which combines VCPA and IRIV, had the best effect on feature selection. VCPA-IRIV extracted 18 feature wavelengths to participate in the modeling instead of the full spectra, and the algorithm can reduce the complexity of the model while maintaining the model’s classification accuracy. In order to improve the classification accuracy of the model, the gravitational search algorithm (GSA) was introduced to search the optimal parameters(c,g) in the SVM model and compared with Grid Search (GS). The results indicated that the VCPA-IRIV-GSA-SVM model has the best classification effect, and the classification accuracy of the test set reached 100%. Thus, it is feasible to use visible near-infrared hyperspectral imaging technology to identify the quality grade of panax notoginseng powder. This method references the quality grade identification of panax notoginseng powder in the market.