Slimming medicines containing illegal additives, often distributed through mail and other delivery channels, are primary targets for public security organizations to curb food, drug, and environmental crimes. To enable rapid identification of such drugs, this study adopted molecular spectral analysis technology to examine slimming medication containing estazolam, sildenafil, sibutramine, flurazepam, and zolpidem; 145 groups of spectral data were obtained. Principal component analysis was utilized to extract the principal component factors and reduce data dimension. Based on the extracted 20-dimensional data, the Fisher discriminant analysis (FDA), K-nearest neighbor (KNN), and support vector machine models were established for comparison. In model 1, three Fisher discriminant functions were constructed to discriminate five types of samples, and the accuracy reached 100%. In model 2, the change of K value will affect the accuracy of the classifier. Through the adjustment of K value, 5 kinds of samples can be classified quickly, with an accuracy rate of 100%. In model 3, RBF kernel function was used to compare the classification effect of zolpidem and other four kinds of slimming drugs, and the accuracy rate reached 100%. Through the dataset in the experiment, the samples of different brands of zolpidem are identified and the actual cases are analyzed, which has a certain reference for the public security organs to investigate such cases.