Near infrared spectroscopy combined with chemometric resolution method was applied to analyze methanol content in methanol diesel quantitatively. In this experiment, 32 samples of methanol diesel in the range of 2%~25.8% (φ) were used for spectroscopy collection and analysis. Three quantitative analysis models of partial least squares (PLS), support vector machine (SVM) and least squares support vector machines (LS-SVM) were established in the range of 4 500~70 000 cm-1. The Radial Basis Function (Radial Basis Function, RBF) was the optimal kernel function of the SVM model compared three different kinds of prediction model during process of building the SVM model. The research results showed correlation coefficient (RP) and the root mean square error of prediction (RMSEP) of PLS, SVM and LS-SVM were 0.985 9, 0.990 3, 0.998 9 and 0.405 2, 0.356 3, 0.062 4 respectively. It was obviously that the three kinds of prediction model could obtain better results especially with LS-SVM modeling. The research results indicated that near infrared spectroscopy combined with chemometrics resolution method could achieve better results when used for detecting methanol content in methanol diesel. Moreover, using near infrared spectroscopy combined with chemometric resolution method to analyze methanol content in methanol diesel quantitatively may have reference value and application potentiality for near infrared spectroscopy quick and non-destructive test of methanol content in methanol diesel.