Abstract: Methanol gasoline and ethanol gasoline, as two distinct types of clean energy sources, possess unique properties and characteristics. Accurate identification of these alcohol-based gasoline types is crucial for ensuring fuel quality and vehicle safety. Near-Infrared Spectroscopy (NIR) is a pivotal method for identifying alcohol-based gasoline types, and modelling based on NIR's effective characteristic wavelengths can overcome the effects of interfering and noisy spectral segments on model accuracy. This study employed the characteristic spectral bands of methanol and ethanol molecules in NIR spectra to develop a Partial Least Squares Discriminant Analysis (PLS-DA) model for distinguishing between methanol and ethanol gasoline. The success rates of full-wavelength spectral, Variable Importance in Projection (VIP) spectral, and feature spectral band models were compared under identical modelling conditions. Results indicated that the full-wavelength and VIP spectral models showed lower accuracy in identifying low-content alcohol-based gasoline samples, with success rates of 90% and 96.7% respectively. In contrast, the optimal feature spectral band model, constructed using the complete differential spectral information of methanol and ethanol molecules (4500～5200+5600～7200+7900～8800 cm⁻¹), achieved a 100% success rate in identifying alcohol-based gasoline samples with volume fractions of 0.5% to 80%. The study demonstrates that selecting feature spectral bands based on chemical structure is an effective wavelength selection method, enhancing model accuracy significantly. In summary, this research successfully establishes a highly accurate model for the qualitative identification of alcohol-based gasoline types using selected NIR spectral bands based on chemical information, showing potential for application in other fuel type identifications.