The contents of arachin, conarachin and subunits significantly affect the gel properties and solubility of peanut proteins, and then affect its application in meat products and beverage. In this study, we collected 178 peanut varieties, measured arachin, conarachin, 23.5 and 37.5 kDa subunits contents by chemical methods. On the basis of peanut sample spectrum scan by near-infrared spectrum technology, we used Partial Least Squares Regression (PLSR) stoichiometry to build a mathematical model with the chemical data. By comparing single and composite spectral pretreatments, model correlation coefficient and errors to value the performance of the models. The best pretreatment method for arachin model was determined as 2nd-der with Detrend, the correlation coefficient of correction (Rc) set was 0.92, and the standard error of calibration (SEC) was 1.41; the best pretreatment method of conarachin model was detrended with 1st-der, the Rc and SEC were 0.85 and 1.46; the best pretreatment method for the 23.5 kDa subunit model was Normalization with 2nd-der, the Rc and SEC were 0.91 and 0.53; Detrend with Baseline was the best pretreatment method for the 37.5 kDa model, the Rc and SEC was 0.91 and 0.53. External validation results showed the Square Errors of Prediction (SEP) of arachin and conarachin were 1.25 and 0.73, respectively. The SEP of 23.5 kDa model and 37.5 kDa model were 0.47 and 0.75 separately. In this study, the contents of arachin, conarachin, 23.5 and 37.5kDa subunits in the whole peanut were detected simultaneously, rapidly and non-destructively based on NIRS. It’s important for the breeding specialist to select special varieties and raw materials for the protein processing industry.