NIR (near-infrared) spectroscopy is a fast, non-destructive quantitative analysis tool. In order to improve the detection of yellow rice wine, NIR is employed for the quantitative analysis. In the detection, due to the varying factors (e. g. environment, raw material, instrument aging), the performance of model developed by the old samples may deteriorate over time. To guarantee the prediction accuracy, the recursive partial least square (RPLS) method is introduced to update the prediction model. However, the whole spectrum used to be involved in the model update, and the number of spectral variables in a whole spectrum is very large, which may result in intensive computation and no obvious improvement in prediction accuracy due to interference information included. Considering the insignificant change of characteristic wavelengths in yellow rice wine production, a model updating method is proposed in this paper based on characteristic wavelength. The correlation coefficient method is employed to extract the characteristic wavelength, and then the RPLS model is developed by incorporating the new sample information in the method. This method is applied to update the NIR detection model of total acid in yellow rice wine. The correlation coefficient r, root mean square errors of prediction (RMSEP) and residual predictive deviation (RPD) are employed to evaluate model performance. These three indices reach 0.965 7, 0.184 3 and 3.736 2 by using the proposed method. Therefore, the proposed method may optimize the model stability, improve the computational efficiency and provide an useful practical reference.