In order to fast analyze the benzene concentration in gasoline, a new measure method based on low-resolution dispersive Raman spectroscopy is proposed. There exist strong measurement noise and fluorescence background in dispersive Raman spectra, so the present paper applies the Savitzky-Golay smoothing filter to remove the measurement noise and uses iterative polynomial curve-fitting to reduce the fluorescence background. Based on ridge regression, principal component regression and partial least squares algorithm, three calibration models of the benzene concentration in gasoline are built and validated by a set of gasoline samples from a refinery. Experimental results show that their repeatability and reproducibility can satisfy the accuracy requirement specified by the standards SH/T0713－2002, regardless of applying what kind of calibration models. In addition to its low cost, small size, convenience to use and so on, the fast measure method based on low-resolution dispersive Raman spectroscopy can be widely applied to the routine analysis.