提出了一种基于多个太赫兹时域光谱系统 (THz-TDS)的物质识别方法。将来自不同 THz-TDS的光谱数据，通过小波变换去除基线及噪声等干扰信息，并用 3次样条插值将不同的采样频率映射到相同的频率上，从而得到标准化后的光谱数据。将此光谱作为支持向量机 (SVM)的特征向量，选择合适的核函数并用网格搜索法寻找最优 SVM参数，最终得到98.33%的识别准确率。该方法对于准确识别物质具有重要的参考价值。

In this paper, we propose a substance identification method based on multiple terahertz time-domain spectroscopy (THz-TDS) systems. Spectral data from different THz-TDS systems were analyzed by wavelet transform to remove the noise and baseline. Then, the standardized data can be obtained by mapping the sampling frequencies to the same with cubic spline interpolation, which is used as the feature vector of the support vector machine (SVM). Finally, the spectral identification accuracy can be up to 98.33% with an appropriate kernel function and the optimization parameters based on grid search. These results are meaningful for the eventual practical application of exact substance identification.