In this study, multivariate analysis methods, including a principal component analysis (PCA) and partial least square (PLS) analysis, were applied to reveal the inner relationship of the key variables in the process of H₂O₂-assisted Na₂CO₃ (HSC) pretreatment of corn stover. A total of 120 pretreatment experiments were implemented at the lab scale under different conditions by varying the particle size of the corn stover and process variables. The results showed that the Na₂CO₃ dosage and pretreatment temperature had a strong influence on lignin removal, whereas pulp refining instrument (PFI) refining and Na₂CO₃ dosage played positive roles in the final total sugar yield. Furthermore, it was found that pretreatment conditions had a more significant impact on the amelioration of pretreatment effectiveness compared with the properties of raw corn stover. In addition, a prediction of the effectiveness of the corn stover HSC pretreatment based on a PLS analysis was conducted for the first time, and the test results of the predictability based on additional pretreatment experiments proved that the developed PLS model achieved a good predictive performance (particularly for the final total sugar yield), indicating that the developed PLS model can be used to predict the effectiveness of HSC pretreatment. Therefore, multivariate analysis can be potentially used to monitor and control the pretreatment process in future large-scale biorefinery applications.