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目前，超材料研究不断向工程化应用推进，在物理机理与效应、设计理论与方法、加工制备与测试等方面取得了突飞猛进的发展。但是，传统的超材料设计主要依赖人工设计和优化，面对大规模的工程化应用设计时，无法实现数量庞大的超材料结构单元的快速整体设计。近几年，涵盖传统启发式算法和神经网络算法的智能算法在超材料设计中所占的比重逐步上升，基于智能算法设计超材料能够打破传统设计方法在不同基材体系、不同频段以及不同性能指标下设计的局限性，展现出快速设计和架构创新的独特优势。该文综述了包括遗传算法、Hopfield网络算法和深度学习在内的几种典型智能算法在超材料设计中的应用，包括正向设计方法和逆向设计方法。基于智能算法能够实现不同性能指标的频率选择表面、多机理复合吸波超材料、平板聚焦超表面以及异常反射超表面的快速设计，为推动超材料技术的工程化应用提供必要设计手段支撑。“,”At present, research on metamaterials is continuously advancing to engineering applications, and great progress is being achieved in the areas of physical mechanisms and effects, design theory and methods, and fabrication and measurement. However, traditional metamaterials design mainly relies on artificial design and optimization. In the face of large-scale engineering applications, it is impossible to realize the rapid overall design of a large number of metamaterial structural units. In recent years, the proportion of intelligent algorithms covering traditional heuristic algorithms and neural network algorithms in metamaterials design has increased gradually. Metamaterials design based on intelligent algorithms can surpass the limitation of traditional methods in different substrate systems, frequency variation, and different performance indicators, offering the unique advantages of rapid design and architectural innovation. This paper summarizes the application of several typical intelligent algorithms, including the genetic algorithm, Hopfield network algorithm, and deep learning algorithm in metamaterials design, which include forward designs and an inverse design. The use of intelligent algorithms can achieve the rapid design of frequency selective surfaces under different performance indexes, multi-mechanisms composite absorber metamaterials, flat focusing, and abnormal reflection metasurfaces, providing the necessary support for design methods while promoting the engineering applications of metamaterials.