Origin is an important environmental factor affecting crop production, and tracing the origin is of great significance for food safety. The chemical analysis method is generally used in traditional agricultural product origin detection, and its operation is cumbersome, destructive and time-consuming. In this study, northern cold mung beans were used as the research object. Near-infrared spectral data of mung bean in two states of seed and powder were obtained in the main origins of high-quality for Baicheng, Dumeng and Tailai. A new nondestructive detecting method for mung bean origins were established by optimizing the NIR characteristic spectrum wavenumbers. Firstly, in the range of 10 105.37~4 078.655 cm-1 wavenumber with strong absorbance value, the raw spectral data of mung beans from different regions was preprocessed by using multivariate scattering correction (MSC) method to eliminate spectral interference information. Then competitive adaptive reweighted sampling(CARS) algorithm is applied to optimize the characteristic spectral wavenumbers of mung bean seed and powder states from different origins to reduce the feature vector dimension of the spectral curve. Finally, a feed-forward neural network (BP) adaptive inference mechanism was used to establish a non-linear mapping model between the origin of mung bean and its spectral characteristic wavenumber, and the encoding vector output by the network was parsed to the original name as the output result of the detection of the origin of the mung bean. The results show that: (1)Preprocessed with multiple scattering corrections in the raw spectral, the error of the spectral curve of mung bean powder is reduced from 12.87 to 3.20, and the error of the spectral curve of mung bean seed is reduced from 153.04 to 27.73, which provides effective and reliable spectral data. (2) Through the competitive adaptive reweighting sampling algorithm, the important characteristic wavenumbers of mung bean spectral curve are extracted. From the 2 114 original wavenumbers of seed and powder state, 61 and 107 characteristic wavenumbers are optimized respectively, and the total number of wavebands is reduced by 94.94%, which is taken as the characteristic index of mung bean origin recognition. (3) The MSC-CARS-BP mung bean origin detection model was put forward innovatively. Based on the optimized spectral characteristic wavenumber as the quantitative basis, the origin detection of mung bean seed and powder was carried out respectively. The accuracy of the prediction set was 92.59% and 98.63%, and the correlation coefficient was above 0.99. This method can use near-infrared spectrum processing technology to achieve the goal of non-destructive detecting of mung bean origin, and provide technical support and reference for automatic and rapid traceability of agricultural products origin.