以药品为研究对象，利用太赫兹时域光谱系统对3种不同药品进行测量并提取折射率、介电常数和物质因子等多个特征参数，然后联合多个特征参数作为输入，采用后向传播(BP)神经网络、支持向量机(SVM)和学习矢量化(LVQ)3种机器学习方法分别对药品进行多特征联合检测分类识别。实验结果表明，多特征联合检测方法识别准确率能够达到95%以上，有效提高药品的检测分类准确度，可用于药品的检测和分类识别。

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.