Visible/near-infrared spectroscopy was applied to determine the content of catalase (CAT) and peroxidase (POD) in barley leaves under the herbicide stress of propyl 4-(2-(4, 6-dimethoxypyrimidin-2-yloxy) benzylamino) benzoate (ZJ0273). The spectral data of the barley leaves in the range of 500~900 nm were preprocessed by moving average with 11 points. Seven outlier samples for CAT and 8 outlier samples for POD were detected and removed by Monte Carlo-partial least squares (MCPLS). PLS, least-squares support vector machine (LS-SVM) and extreme learning machine (ELM) models were built for both CAT and POD. ELM model obtained best results for CAT, with correlation coefficient of calibration (Rc) of 0.916 and correlation coefficient of prediction (Rp) of 0.786. PLS model obtained best prediction results for POD, with Rc of 0.984 and Rp of 0.876. Successive projections algorithm (SPA) was applied to select 8 and 19 effective wavelengths for CAT and POD, respectively. PLS, LS-SVM and ELM models were built using the selected effective wavelengths of CAT and POD. ELM model performed best for CAT and POD prediction, with Rc of 0.928 and Rp of 0.790 for CAT and Rc of 0.965 and Rp of 0.941 for POD. The prediction results using the full spectral data and the effective wavelengths were quite close, and the prediction performance for POD was much better than the prediction performance for CAT, and the studies should be further explored to build more precise and more robust models for CAT and POD determination. The overall results indicated that it was feasible to use visible/near-infrared spectroscopy for CAT and POD content determination in barley leaves under the stress of ZJ0273.