As an important traditional Chinese medicines (TCMs) for promoting qi, activating blood, and relieving pain, Curcuma has attracted wide attention in recent years. In order to identify and control the quality of four origins of Curcuma, the terahertz time-domain spectroscopy combined with chemometric method (support vector machine method and principal component analysis method) was used to classify and identify the four origins of Curcuma. In this study, three models of slope loss multi-class support vector machine methods (Ramp Loss K-SVC method), random forest (RF), and extreme learning machine algorithm (ELM) were constructed to distinguish Curcuma with four different origins. It was developed the Ramp Loss K-SVC method and optimized the model parameters that the identification rate of the four types of Curcuma were increased to 93%. This paper provides a new identification technique for the identification of four easily confused origins.