Based on the Terahertz Time-Domain Spectrum(THz-TDS) technology, a multi-feature joint medicine inspection method is proposed to study three different medicines. First, the measurement data is acquired with a THz-TDS system. For consistency, medicines are dried, crushed into powder and then made into capsules. Then, different features like refractive index and material factor are obtained by using feature extraction method. Finally, medicines are classified and identified by a multi-feature joint detection method, in which three different machine learning methods, Back Propagation(BP) neural network, Support Vector Machine(SVM) and Learning Vectorization Quantization(LVQ), are adopted to improve the efficiency and accuracy. In the training process, all parameters are combined as the training sample in order to improve the characteristic ability. Experiment results show that the accuracy with machine learning are above 95%, and for SVM, the accuracy reaches 99%. The results confirm the application of the terahertz multi-feature joint method in medicine quality inspection and identification.