物质的太赫兹光谱具有独特的“指纹谱”特性,可以利用该特性对物质进行识别。随着人工智能技术的发展,深度学习算法在太赫兹光谱识别领域得到了越来越广泛的应用。然而在实际应用中,受实验设备、实验条件以及实验环境等因素的影响,所获取的太赫兹光谱数据并不总是大规模的,无法满足深度学习算法所需的数据量要求。为了解决这一问题,本文提出了一种基于生成对抗网络（GAN）的太赫兹光谱识别方法。首先利用S-G滤波器和三次样条插值法对物质的太赫兹光谱数据进行预处理,然后通过GAN生成具有真实太赫兹光谱数据分布的仿真数据,最后将生成的数据以及真实光谱数据作为训练样本对深层神经网络进行训练,从而得出物质的识别结果。实验结果表明：GAN模型生成的太赫兹光谱数据可以有效地模拟真实太赫兹光谱数据的总体特征,扩充太赫兹光谱数据样本,极大地提高了光谱的识别精度。

Due to the unique "fingerprint spectrum" characteristic, terahertz （THz） spectrum can be used to recognize the materials. With the development of artificial intelligence, deep learning is widely used in the field of THz spectrum recognition. However, the acquired THz spectral data are not always on a large scale due to the influence of experimental equipment, conditions and environment, which cannot meet the data size requirements of the deep learning algorithm. In order to solve this problem, we proposed a method of THz spectrum recognition based on generative adversarial networks （GAN） in this paper. Firstly, an S-G filter and a cubic spline interpolation method were employed to pre-process the THz spectral data. Secondly, the simulation data with the distribution of real THz spectral data were generated by the GAN. Finally, the generated data and real spectral data were taken as the training samples to train the deep neural networks （DNN）, thus obtaining the recognition results of the materials. The experimental results show that the THz spectral data generated by the GAN model can effectively simulate the overall characteristics of real THz spectral data and expand the THz spectral data samples, greatly elevating the spectral recognition accuracy.