Four soybean paste samples were selected to establish a rapid identification method by infrared spectrum technique. Abnormal samples were discarded and all samples were through pre-treatment. Discriminant Partial Least Squares( DPLS) 、Soft Independent Modeling of Class Analogy( SIMCA) and Back Propagation Neural Net( BP-ANN) were used to identify four kinds of Soybean Paste. The results showed that the classification rate of calibration was 99. 10% 、98. 20% and 100%; the validation rate was 94. 55% 、89. 09% 、90. 91% respectively. By comparing three recognition algorithms,DPLS was superior to the other two on calibration and validation rate. The study showed that the near infrared spectroscopy technology is a feasible way for the soybean paste classification.