Smilax glabra Roxb. (SGR) is known for its high nutritional and therapeutic value. However, the frequent appearance of counterfeit products causes confusion and inconsistent quality among SGR varieties. Herein, this study collected the proportion of SGR adulteration and used high-performance liquid chromatography (HPLC) to measure the astilbin content of SGR. Then Fourier-transform near-infrared (FT-NIR) technology, combined with multivariate intelligent algorithms, was used to establish partial least squares regression quantitative models for detecting SGR adulteration and measuring astilbin content, respectively. The method conducted a quantitative analysis of dual indicators through single-spectrum data acquisition (QADS) to comprehensively evaluate the authenticity and superiority of SGR. The coefficients of determination (R2) for both the calibration and prediction sets exceeded 0.96, which successfully leverages FT-NIR combined with multivariate intelligent algorithms to considerably enhance the accuracy and reliability of quantitative models. Overall, this research holds substantial value in the comprehensive quality evaluation in functional health foods.