Abstract: Based on near-infrared spectroscopy，a non-destructive testing method to achieve rapid and non-destructive classification of pesticide residues on vegetables was proposed.It hope to realize the classification and detection of pesticide residues on vegetables.By studing the lettuce samples sprayed with cypermethrin solution，triazophos solution and unsprayed pesticide，the modeling effects of different pretreatments were compared，and SNV algorithm was chosen as the optimal pre-processing method for this study.In this study，the successive projection algorithm（SPA），the bootstrapping soft shrinkage（BOSS）and the competitive adaptive reweighted sampling（CARS）were used to select the characteristic bands of the preprocessed spectral data.A support vector machine（SVM）and the support vector machine（SVM）algorithm based on Grey wolf optimization（GWO）optimization were used to establish a classification model of characteristic wavelength variables.The results showed that the CARS-GWO-SVM model achieved the best classification influence，and the accuracy of training set and the accuracy of prediction set were both 100%.Therefore，it is feasible to use NIR spectroscopy to classify pesticide residues on vegetables.This study also provided a reference for the rapid，non-destructive analysis of other pesticide residues in lettuce.