Hyperspectral images of rice from northeast/non-northeast regions were collected, and spectral images at characteristic wavelengths were screened. The clustering combination of image features and pattern recognition method was established to quickly and accurately identify northeast/non-northeast rice origin. Northeast rice is mainly japonica rice, and the typical northeastern rice varieties include long-grain, round-grain, rice flower and Xiaoding rice. Considering the practicability and applicability of rice origin identification model, samples of 10 origins and 4 varieties above were collected to form the original sample set. Among them, there are five northeastern origins, including Heilongjiang (1), Jilin (2), Liaoning (2), and five non-northeastern origins, including Hebei (1), Zhejiang (1), Jiangsu (2) and Anhui (1). 100 samples were selected randomly from each producing area. Hyperspectral images of 100×10 rice samples were collected using SisuCHEMA hyperspectral imaging system （Specim, Finland）in the range of 900～1 700 nm. Extracting the average spectra of a single rice sample by selecting the region of interest according to the rice contour, Kennard-Stone method was used to divide training set and test set according to the ratio of 4∶1. Eight characteristic wavelengths were screened by Successive Projections Algorithm（SPA）: 1 460.30, 1 400.20, 1 424.92, 945.98, 1 315.62, 1 220.87, 1 705.91, 942.53 nm. The eight models were built respectively by HOG features extracted from single characteristic wavelength Image and SVM to identify the rice origin whether it was from northeast or non-northeast China. The recognition accuracy was as follows: 85.5%, 77.5%, 76.5%, 73.5%, 71%, 68.5%, 67%, 65.5%. In view of the low recognition rate of single model, a strategy of establishing model cluster based on single characteristic wavelength image model to synthetically discriminate rice origin was proposed. According to the recognition rate of single model from high to low, the cluster models were established by respectively combining three, five and seven the signal models above. While the probability of the sample judged to be true predicted by the conjunctive model is greater than 50%, the sample will be judged to be true, otherwise it will be false. The experimental results showed that the recognition rate of the test set samples can reach 90.5% by combining the model sets of 1 460.30, 1 400.20, 1 424.92, 945.98, 1 315.62, 1 220.87 and 1 705.91 nm bands. This study shows that hyperspectral technology combined with the strategy of conjunctive model consensus can provide feasible and effective methods to establish a robust and wide applicability model to recognize the rice origin (northeast/non-northeast) rapidly.