The quality of the bacterial strain cultivation in the seed tank during the citric acid fermentation process directly affects the fermentation level. Hence, it is crucial to accurately and rapidly detect the quality parameters of the culture solution in the seed tank. However, these parameters are currently largely measured manually, which does not meet real-time monitoring and precise control requirements. This paper builds a chemometric model for measuring the total acidity (TA) and reducing sugars (RS) in the seed tank’s culture solution, based on near-infrared spectroscopy. Initially, the original spectra were analyzed, and to eliminate random noise and reduce batch variability effects on the sample spectra, the SG-DT method of smoothing (SG) and detrending (DT) were sequentially used for spectral preprocessing. Then, the Interval Partial Least Squares (iPLS) method was used for feature wavelength selection, the effect of different division intervals on the selection result was discussed, and the optimal division interval number for the target quality parameter of TA was determined to be 21, with 495 feature wavelengths. For RS, it was 20, with 361 feature wavelengths. Subsequently, the correlation between spectral variables and quality parameter variables was analyzed. A BP network was introduced to establish the calibration model for TA, and both PLSR and BP networks were used to establish the calibration model for RS, and model prediction effects were compared to determine the optimal model. Finally, the optimal prediction model for TA based on the BP network had an of 0.808 5 and an RMSEP of 0.123 4. The model prediction effect of RS based on the BP network was superior to the PLSR model, with an of 0.964 7 and RMSEP of 0.173 9. This paper has realized online prediction of multiple quality parameters during the bacterial strain cultivation process in the complex citric acid fermentation system, providing a basis for real-time intelligent control of the fermentation process.