NIR quantitative modeling mainly involves four process parameters: the selection of sample set, spectra pre-processing, latent variables and variable selection methods. And in the traditional method, the optimization of the PLS modeling parameters was step by step according to the model evaluation indexes and the inter-influence among the parameters was rarely considered in the model development. This is risky because the modeling path is not necessarily the best approach to step by step optimization. The system modeling method is global trajectory parameter optimization idea based on the correlation between the parameters, which is a systematic approach to improve the efficiency and accuracy in the development of a quantitative model. The purpose of this study is based on the system modeling idea to develop a NIR quantitative model for the analysis of salvianolic acid B in Naoxintong capsule. The content of salvianolic acid B in 56 samples of Salvia miltiorrhiza was determined by high-performance liquid chromatography (HPLC), and the near-infrared spectra were also collected. D-optimization design method was used to optimize the modeling parameters including the sample set partition, spectral pre-processing, latent variable factor number and variable selection. The global optimal parameter trajectory was applied in the preparation of the quantitative analysis model. The results showed that the best quantitative model was developed by the spectral data pretreatment of 2D+SNV (2^nd Derivative + Standard Normal Variate), choosing the latent variable factor number of 7 and spectral region of 4 000~10 000 cm^-1 in combination with the 56 samples were randomly divided into a calibration set and a validation set according to the proportion of 3:1 by a K-S algorithm. Its standard deviation of calibration (RMSEC) and prediction (RMSEP) were both 0.001 8, which demonstrated the high analytical accuracy and robust fitting. The calibration coefficient of determination (R²_cal) and the prediction coefficient of determination (R²_pre) were 0.994 0 and 0.995 2, respectively. The ratio of the standard error of prediction to the standard deviation (RPD) was 9.19, further confirming that the model can be used for high-quality quantitative analysis. Based on the system modeling idea, the D-optimal design approach was adopted to implement the global trajectory parameter optimization in the process of establishing the quantitative model in this paper. Furthermore, a quantitative model for salvianolic acid B was developed with robust predictability and high accuracy. The model provided an efficient and rapid method to quantify salvianolic acid B of Naoxintong capsule, which was of great significance for the quality control of intermediates and products of Naoxintong capsule.