隐身涂料通过对雷达波、红外辐射、可见光及激光信号特性的调控，广泛应用于军事装备与先进技术领域。然而，隐身涂料的设计涉及多种材料和复杂加工参数的耗时实验。为了克服这些限制，数据驱动的涂料设计方法受到广泛关注。文章综述了基于机器学习的隐身涂料设计的最新进展。概括了隐身涂料的主要类型，包括吸波涂料、电磁屏蔽涂料、红外隐身涂料和复合隐身涂料，探讨了传统设计方法面临的挑战。介绍了数据驱动的隐身涂料设计，展示了数据预处理与特征提取策略如何优化模型输入，强调了高质量数据库、模型可解释性与多目标优化的重要性。此外，总结了机器学习在隐身涂料性能预测、材料筛选、结构设计及逆向优化等方面的研究案例。最后，探讨了各领域数据驱动下功能涂料的最新研究，为隐身涂料的智能设计提供参考。

Stealth coatings，by regulating radar waves，infrared radiation，visible light，and laser signals，were widely applied in military equipment and advanced technological fields. However，the design of stealth coatings involved time-consuming experiments due to thecomplexity of material selection and processing parameters. To address these limitations，data-driven coatings design methods had attracted increasing attention. This review highlightedrecent advances in stealth coatings design based on machine learning. It summarized the maintypes of stealth coatings，including radar-absorbing，electromagnetic shielding，infrared stealth，and composite stealth coatings，while discussing the challenges of traditional designmethods. The review introduced data-driven stealth coatings design approaches，demonstratinghow data preprocessing and feature extraction strategied optimize model inputs. It underscored the significance of high-quality databases，model interpretability，and multi-objective optimization. Additionally，research cases were presented where machine learning had been applied in performance prediction，material screening，structural design，and inversed optimization of stealth coatings. Finally，recent data-driven research advancements in functional coatings across various fields were explored，providing valuable insights into the intelligent design of future stealth coatings.