AbstractTo explore the best method for non‐destructively and accurately quantitative detection of mixed pesticide residue in vegetables, the mixed pesticide (fenvalerate and dimethoate) of lettuce leaves was used as the research object detected by hyperspectral technique. The hyperspectral data was preprocessed using standard normalized variable. Then two different kinds of characteristic wavelengths were selected using competitive adaptive reweighed sampling (CARS) and random forest‐recursive feature elimination (RF‐RFE), respectively. The least squares support vector regression (LSSVR) model results of predicting fenvalerate and dimethoate showed that the CARS‐screened wavelengths had the best modeling results for fenvalerate with of 0.8203 and RMSEP of 0.0222, and the RF‐RFE‐screened wavelengths had the best results for dimethoate with of 0.8712 and RMSEP of 0.0186. To simplify the calibration model, successive projections algorithm (SPA) was used for the second selection of characteristic wavelengths. Finally, the CARS‐SPA‐LSSVR model for predicting fenvalerate achieved the accuracy with of 0.8890 and RMSEP of 0.0182, RF‐RFE‐SPA‐LSSVR model for predicting dimethoate with of 0.9386 and RMSEP of 0.0077. Thus, hyperspectral technique can be applied to quantitatively detect the mixed pesticide residue of lettuce leaves.Practical applicationsLettuce leaves can be used for raw food, and lettuce leaves with mixed pesticide residues is more likely to bring harm to people's health. Traditional methods of detecting pesticide residues are time‐consuming and destructive to samples, so they cannot meet the requirement of modern agriculture. Whereas, hyperspectral imaging technology is a new rapidly growing method which integrates spectroscopic and imaging techniques in one system for providing both spectral and spatial information simultaneously, of which the spectral information can detect the physical structure and chemical composition of unknown samples. Therefore, the hyperspectral technique is a fast and nondestructive detecting method, and this study showed it is feasible for quantitative detection of the mixed pesticide residue of lettuce leaves.