利用全波分析方法计算了不同电路板加载、不同孔缝和尺寸的开孔金属腔在0～5 GHz范围内的屏蔽效能(SE)，获得共计5250个样本。进而利用机器学习中的随机森林回归算法，对其中4200个样本数据进行训练，获得了可以根据开孔腔物理尺寸、加载物材料及电磁特性和位置、频率等共计16个输入参数快速评估开孔加载金属腔屏蔽效能的机器学习模型。利用其余的1050个样本进行模型验证，结果表明该模型可以快速准确地计算加载腔的电磁屏蔽效能。该模型具有随时根据样本量增加不断训练提高其普适性的特点，可为实际工程中加载开孔腔的屏蔽设计及SE评估提供高效途径。

A machine learning based evaluation method for shielding effectiveness(SE) of loaded metallic enclosures with apertures under electromagnetic wave radiation is proposed. The SEs of a variety of metallic enclosures loaded with different printed circuit boards (PCBs) is calculated using full wave analysis simulation in the frequency range of 0-5 GHz, and 5250 samples are obtained. The random forest model which is one of the popular machine learning aggression algorithms is employed to train stochastically the selected 4200 samples. Consequently, the model capable to fast predict the SE for loaded shielding enclosures characterized by 16 parameters is implemented. The rest 1050 samples are used to verify the proposed random forest model. Results show that the proposed model can quickly predict the electromagnetic shielding effectiveness of the enclosure loaded with PCBs.