Objective To rapidly quantify four major organic acids in 47 base wines of Fuyuxiangxing crude Baijiu using near-infrared spectroscopy (NIR) and chemometrics.Methods The study used the Kennard-Stone (K-S) algorithm to divide the samples, combined with preprocessing strategies such as normalization, standard normal variate transformation (SNV), and Savitzky-Golay smoothing. Model parameters were optimized through cross-validation, and the effects of the number of principal components and band selection on the performance of the principal component regression (PCR) and partial least squares regression (PLSR) models were evaluated.Results The validation results showed that the prediction correlation coefficients (Rc2) of the PCR models for the four major organic acids (acetic acid, n-butyric acid, n-pentanoic acid, and n-hexanoic acid) were all higher than 0.9, and the root mean square errors of prediction (RMSEPs) were all lower than 7. The Rc2 values of the PLSR models were all higher than 0.8, and the RMSEPs were all lower than 8, indicating that the models had good generalization ability and prediction accuracy.Conclusion NIR combined with PCR and PLSR modeling methods can effectively achieve the rapid quantitative analysis of the four organic acids in Fuyuxiangxing crude Baijiu base wine.