柴油机作为一种多输入多输出的高复杂度与耦合度系统，难以用精确的物理与化学模型准确描述。通过空间填充设计采集训练数据集，采用GBDT(Gradient Boosting Decision Tree,梯度提升决策树)算法构建了柴油机有效燃油消耗率（BSFC）、NO_x和CO预测模型，并对模型进行了验证。结果表明：预测模型收敛速度较快；BSFC，NO_x，CO拟合程度R²分别为0.981，0.993，0.992；预测值平均相对误差为0.81%，3.68%，2.95%；模型生成的BSFC,NO_x,CO响应与真实柴油机趋势具有一致性；预测模型有较高的精确度和稳定性。梯度提升决策树算法对柴油机建模有较高的适应度，能够有效解决多特征高维非线性柴油机系统问题，为柴油机性能预测建模提供了一种行之有效的方法。

As a high complexity and coupling system with multi-input and multi-output, diesel engine is difficult to describe accurately with accurate physical and chemical models. Training data sets were collected through space-filling design, and the BSFC, NO_x and CO prediction models were built by GBDT (Gradient Boosting Decision Tree) algorithm and further verified. The results show that the convergence velocity of prediction model is fast. The fitting degree R² of BSFC, NO_x and CO are 0.981, 0.993 and 0.992 respectively, and the average relative errors of predicted values are 0.81%, 3.68% and 2.95% respectively. The responses of BSFC, NO_x and CO generated by the model are consistent with the trends of real diesel engine. The prediction model has high accuracy and stability. The gradient lifting decision tree algorithm has high adaptability to diesel engine modeling, and can effectively solve the problems of multi-feature high-dimensional nonlinear diesel engine system, which provides an effective method for diesel engine performance prediction modeling.