针对百叶窗翅片换热器性能预测问题，系统对比了多种机器学习模型的预测能力。通过计算流体力学（CFD）数值模拟构建了包含343组数据的数据集，涵盖翅片角度、间距和长度三个关键几何参数，并基于j因子、f因子和jf因子评估换热性能。研究选取了线性模型、树模型、集成模型、神经网络等共13种机器学习方法，采用R²和RMSE作为评估指标。结果表明，集成学习模型，尤其是CatBoost模型，在各项预测任务中均表现最优，测试集R²均高于0.997，RMSE控制最为稳定，展现出卓越的拟合能力与泛化性能。此外，通过SHAP值分析揭示了各几何参数及其耦合作用对性能因子的影响机制，为后续换热器的高效设计提供了参考依据。

Aiming at the performance prediction of louvered fin heat exchangers, the predictive capabilities of various machine learning models were systematically compared. A dataset containing 343 data points was constructed through computational fluid dynamics (CFD) numerical simulations, covering three key geometric parameters sucn as fin angle, spacing and length. Heat transfer performance was further evaluated based on the j-factor, f-factor, and jf-factor. A total of 13 machine learning methods were selected, including linear models, tree-based models, ensemble models, and neural networks, with R² and RMSE used as evaluation metrics. The results showed that ensemble learning models, particularly CatBoost, performed best across all prediction tasks, with test set R² values all above 0.997 and the most stable control was RMSE, demonstrating excellent fitting capability and generalization performance. Furthermore, SHAP value analysis was conducted to reveal the influencing mechanisms of different geometric parameters and their coupling on the performance factors, providing insights for the efficient design of future heat exchangers.