【摘要】 真空电子器件传统的设计方法面临多物理场强非线性耦合与高维参数空间搜索效率低下的双重制约。机器学习凭借高维非线性映射与数据驱动能力，推动了该领域由经验指导型向数据与物理协同驱动型演进。文章系统梳理了机器学习在真空电子器件核心部件中的研究进展与应用架构。首先，归纳了机器学习在电磁器件设计领域的研究进展。其次，依据核心组件分类，深入剖析了机器学习在正向预测代理加速、多目标进化优化、智能逆向设计及运行状态诊断中的关键技术路径，阐明了其在压缩研发周期、均衡性能指标及增强鲁棒性方面的优势。更多还原

【Abstract】 Conventional design methods for vacuum electronic devices are limited by strongly nonlinear multiphysics couplings and inefficient exploration of high-dimensional parameter spaces. Machine learning is driving a shift from experience-based design to data-physics collaborative design with its ability of high-dimensional nonlinear mapping and data-driven learning. A systematic review of machine learning applications in core vacuum electronic device components is presented. Recent advances in electromagnetic device design are first summarized. Key technical approaches are then analyzed by component category, including surrogate-based forward prediction, multi-objective evolutionary optimization, intelligent inverse design, and operational state diagnosis. The results demonstrate that machine learning can effectively reduce design cycles, balance competing performance metrics, and improve system robustness.更多还原