At present, traditional liquor selection commonly employs the method of “liquor picking by flowers” during production, relying on workers’ subjective experience for evaluation. However, multiple influencing factors affect the actual production, resulting in uncertainty in the process of liquor connection, posing challenges in ensuring the stability of original liquor quality. This study collected samples of the original and composite original liquor with varying concentrations of tail liquor (0.0%~2.0%). The three-dimensional fluorescence spectra were obtained by fluorescence scanning, establishing a correlation between substance changes and fluorescence data. The fluorescence spectra underwent pre-processing steps such as removing scattering, Raman normalization, Savitzky-Golay smoothing, and removing outliers. Subsequently, parallel factor analysis was used to decompose the spectra into four uncorrelated components, and these components were initially identified through composite similarity analysis in conjunction with the attributes observed in single-substance fluorescence spectra. The results show a higher correlation between the fluorescence spectra of most acids and esters with component 2, suggesting that acids and esters have a stronger influence on the fluorescence properties of component 2. The dataset is reduced from 781×61×164 to 4×164, achieving data dimensionality reduction. A support vector machine (SVM) model was developed to assess the quality of the original liquor. A genetic algorithm (GA) was also employed to optimize the SVM model. GA-SVM model performs better than the original SVM model in accuracy and precision. The optimized model achieved an accuracy of 88.64% compared to 95.45% of the original model, and the precision improved from 0.94 to 1.00. This suggests that integrating 3-D fluorescence and chemometrics is an effective method for rapid detection to evaluate the quality of the original liquor. And provide support for online detection during the distillate liquor selection process, thereby enhancing the overall quality control.