Abstract: In the case of a large numbers of samples participating in the modeling, classification modeling on the sample set could reduce the range of variation of the sample, and improve the prediction capability of the model. In this paper, 222 wheat samples across China were used as modeling sample. Combined with near-infrared spectral information of samples, the sample set was classified by probing-based unknown classes samples clustering methods (nearest neighbor approximation and maximum-minimum distance algorithm)，under the condition that the component content of samples and type of ownership were unknown. When the threshold of the nearest neighbor approximation algorithm was 1.9, and the threshold of the maximum-minimum distance algorithm was half of the maximum distance, the classification model indicators were better than unclassified model. The classification process and results indicated that many times of training was not a necessity with the probing-based method of sample clustering with unknown categories, but the classification threshold need to be determined, the classification changing accordingly with different threshold values. This study provided a reference method for unknown category sample classification during the near-infrared modeling process.