Near infrared (NIR) spectroscopy and chemometrics were applied to determine the effect of pathlength variation on the spectra of the Chinese rice wine and the consequences of the prediction precision of calibration models developed for measuring alcoholic degree, sugar content, and pH was investigated in the present research. Samples were scanned in transmission mode using a commercial FT-NIR spectrometer and a demountable liquid cell for versatile path length liquid sampling. By comparing the results of performance between models based on different optical pathlength (0.5, 1, 1.5, 2, 2.5, and 3 mm), the best indicators of optical pathlength were identified. Based on the optimum pathlength, the models for alcoholic degree, sugar content and pH were established. The best optical pathlength for the alcoholic degree was 2 mm, using partial least squares regression (PLSR) model with the original spectra, correlation coefficient (r) was 0.942, root mean standard error of calibration (RMSEC) and root mean standard error of cross-validation (RMESCV) were 0.256 (％,(φ)) and 0.292 (％,(φ)), respectively; the best optical pathlength for the sugar content was 1 mm, using PLSR model with the original spectra, r was 0.945, and RMSEC and RMESCV were 0.125％ and 0.149％, respectively; the best optical pathlength for the pH was 2 mm, using PLSR model with the original spectra, r was 0.947, and RMSEC and RMESCV were 0.018 and 0.039, respectively. This study showed that pathlength variation had influence on the performance of calibration models for Chinese rice wine, and a suitable pathlength could effectively improve detection accuracy.