Lycium barbarum produced in Ningxia belongs to the genuine regional drugs contained in the Pharmacopoeia of the People's Republic of China. Due to the small planting area, low yield, high medicinal value and high consumer preference, the market is filled with chaos, and the phenomenon of passing others origins off as Ningxia happens occasionally. Therefore, it is of considerable significance to establish a rapid and effective geographical origin identification model of Lycium barbarum to supervise the market. In the process of market transactions, discriminating origin of Lycium barbarum is often based on experience, which has much error and low credibility. The traditional physical and chemical experiment has a long identification cycle and can't be operated by non-professionals. In recent years, some scholars have found that the content of Lycium barbarum in different producing areas is different. However, because of the small sample size, irregular shape and uneven distribution of components, the near-infrared spectroscopy identification technique often needed to smash Lycium barbarum to collect spectral information. Near-infrared hyperspectral image technology combined with near-infrared spectroscopy and image technology, which contains rich spatial information and spectral information, can achieve non-destructive acquisition of spectral information. In this research, near-infrared hyperspectral image technology was used to discriminate the geographical origin of Lycium barbarum samples, which were gathered from Gansu, Qinghai, Xinjiang, Ningxia and Inner Mongolia in China. After collecting the hyperspectral information of 1 650 samples by hyperspectral image system, the region of interest (ROI) was effectively extracted by threshold image segmentation and denoising. During the pretreatment process, the comparison between zero-phase component analysis (ZCA) whitening results and normalization results indicated that ZCA whitening was an effective spectral preprocessing method to remove correlation between features and improve the accuracy of the model. Partial least squares based dimension reduction (PLSDR) was used to reduce the complexity of the model for the preprocessed data. The experimental results indicated that the data after ZCA whitening pretreatment could be reduced from 288-dimensional features to 4 principal components, which made the correlation-removed features can be represented by fewer hidden features. Finally, the dimensionality-reduced features were fed to different classifiers to train model, including support vector machine (SVM), linear discriminant analysis (LDA) and softmax regression. Among those models, the average recognition accuracy based on ZCA whitening, PLSDR and softmax regression was 94.06% on the test set. The results demonstrated that the proposed method could effectively discriminate the origin of Lycium barbarum samples.