To prevent criminals from using various delivery channels to transport weight loss drugs doped with toxic and harmful non-food raw materials, a pattern recognition method for weight loss drugs based on terahertz time-domain spectroscopy is proposed in this study. Compared with traditional methods, terahertz spectrum has a high signal-to-noise ratio in time-domain, which is fast, time-saving, and lossless. In this study, seven weight loss drug types were selected as experimental samples. The terahertz time-domain spectra of the samples were collected; accordingly, three characteristic frequency intervals of 0-0.19 THz, 1.75-2.14 THz, and 2.23-2.5 THz were detected by the automatic peak finder. The characteristic frequency intervals were processed using the Hilbert transform, Butterworth low-pass filter, fast Fourier transform low-pass filter, and first derivative after standard normal transform. Subsequently, the obtained feature data was fused with the original spectrum. The original data and the data fused by the four methods were classified and recognized using particle swarm optimization least squares support vector machine and random forest models. The experimental results demonstrate that the particle swarm optimization least squares support vector machine model has the best recognition effect on the spectral feature fusion data after Hilbert transform, whose accuracy can reach 100%. This approach can be used as a reference for the identification of weight loss drugs in forensic science.