Black Goji berry contains various nutrients such as cyanidin, polysaccharides, trace elements and so on, and has extremely high economic and medical value, the similar Nitraria Tanggu impersonates in the market. The market price of Nitraria Tangguis low. Hyperspectral image technology combines image and spectrum in one, commonly used in food detection and recognition. This study combined with hyperspectral image technology to non-destructively identify Black Goji Berry and nitraria tanggu. Hyperspectral reflection spectra of Black Goji Berry (180) and nitraria Tanggu (180) in the range of 900~1 700 nm were collected respectively, a total of 254 bands. Removing the first 22 abnormal bands and using the last 232 bands as model inputs. Kennard-Stone method is used to divide samples, correction set:prediction set=2:1. The successive projections algorithm (SPA) method is used for spectral dimensionality reduction, setting the characteristic wavelength range to 0~30, which extracts 20 characteristic wavelengths. The full spectrum and 20 characteristic wavelengths extracted by SPA are used as model inputs to establish support vector machine (SVM) and extreme learning machine (ELM) models to identify Black Goji Berry and nitraria Tanggu. The results show that the recognition rates of the SVM model based on FS and SPA are both 100%, the recognition rates of the ELM model based on FS and SPA are both 100%, the SPA method can reduce model input without reducing the accuracy of model recognition. The input is only 8.62% of FS, which greatly reduces the number of model calculations. This study provides a theoretical basis for identifying Black Goji Berry and nitraria Tanggu.