In this paper, we report the combination of a near-infrared (NIR) spectroscopic method with multivariate analysis in order to develop a calibration model of the saccharification ratio of chemically pretreated Erianthus. The regression models clearly depend on the NIR spectral regions, and the information of CH and aromatic framework vibrations contributed most effectively to the alkaline dataset. From interpretations of the regression coefficient, lignin and cellulose were negatively and positively correlated with the saccharification ratio, respectively, and this result was supported by the data from wet chemical analysis. A more complex dataset was obtained from varied chemical pretreatments; here, the saccharification ratio was either small or had no linear correlation with each structural monocomponent. These results enabled the successful construction of the PLS regression model. NIR spectroscopy can be a rapid screening method for the saccharification ratio, and furthermore, can provide information of the key factors influencing the realization of more efficient enzymatic accessibility.