为了提升太赫兹光谱分析的瞬时性和系统集成度，提出一种超表面光谱编码与计算重建相结合的太赫兹光谱测量方法。利用多种超表面结构的光谱编码器件对入射太赫兹波进行高随机光谱编码，并提出基于字典学习的稀疏恢复算法以实现光谱重建。理论计算和数值仿真表明，在4%噪声水平下，所提方法对乳糖透射光谱的重建误差小于3%，可为片上集成化太赫兹光谱仪的发展提供新途径。

In order to improve the transient performance and system integration level of terahertz spectral analysis, this study proposes a novel terahertz spectrum-measurement method that combines metasurface spectral encoding and computational reconstruction. High-random spectral encoding of incident terahertz waves is achieved using spectral encoder devices that utilize multiple metasurface structures. Furthermore, a sparse recovery algorithm based on dictionary learning is developed to accurately restore the spectrum. Theoretical calculations and numerical simulations demonstrate that under a 4% noise level, the proposed method achieves a reconstruction error of less than 3% for the lactose transmission spectrum. Thus, the proposed method provides a new pathway for developing on-chip integrated terahertz spectrometers.