N-butanol is considered an ideal diesel additive because of its good solubility, low volatility, low price and corrosiveness. The accurate quantitative analysis of n-butanol in diesel has important scientific significance and practical value for its quality evaluation and market supervision. This paper proposes a rapid quantitative analysis method for n-butanol in diesel based on Raman spectroscopy combined with partial least squares (PLS). Firstly, Raman spectra of 40 diesel samples were collected, and the effects of different pretreatment methods (first derivative, second derivative, multivariate scattering correction, standard normal transform, Normalization and wavelet transform) on the prediction performance of the PLS calibration model were investigated. Secondly, variable importance in projection (VIP) is used to extract characteristic variables from the spectral data preprocessed by the Normalization method, and the threshold of VIP is optimized by five-fold cross-validation. Finally, based on the optimal spectral pretreatment method, input variables and model parameters, a PLS calibration model was built to analyze the content of n-butanol in diesel quantitatively. The prediction performance was compared with the RAW-PLS and Normalization-PLS models. The results show that the Normalization-VIP-PLS calibration model has excellent predictive performance (R2CV and RMSECV are 0.998 4 and 0.236 2%, R2P and RMSEP are 0.998 7 and 0.208 4%; RSD 0.035 5). Therefore, this paper successfully established a rapid quantitative analysis method of n-butanol in a diesel by Raman spectroscopy combined with the PLS algorithm. This method has the advantages of being fast, accurate and convenient and it can provide new ideas and methods for the detection and quality analysis of diesel and other fuel additives.