交通事故对公共安全构成重大风险，是交通运输系统中的重要问题。准确预测事故严重程度对于采取有效的预防和干预措施至关重要。提出了一种基于集成学习的方法，将XGBoost和MLP两种先进算法相结合，以更精准地预测交通事故的严重程度。建立了一个堆叠分类器，并详细评估了其在交通事故预测中的性能。实验结果表明，该集成模型相较于传统XGBoost模型，在预测准确性上有明显提升，在宏平均F₁分数上显著提高了20.41%。展示了模型优势与创新性，包括模型集成与网络改造。此外，还分析了影响预测结果的关键特征，并探讨了模型在实际应用中的潜在价值。该研究为交通安全管理提供了更科学、更高效的决策支持，有望在交通管理、智能驾驶等领域发挥重要作用。

Traffic accidents pose significant risks to public safety and represent a critical issue in transportation systems. The accurate prediction of accident severity is essential for implementing effective prevention and intervention measures. An ensemble learning approach, combining the advanced algorithms XGBoost and MLP, was proposed to enhance the accuracy of traffic accident severity predictions. A stacked classifier was established and its performance in traffic accident prediction was thoroughly evaluated. The experimental results demonstrate that the integrated model significantly improves prediction accuracy compared to the traditional XGBoost model, with a notable 20.41% increase in the macro-average F₁ score. The advantages and innovations of the model, including model integration and network transformation, were highlighted. Additionally, the key features affecting the prediction results were analyzed, and the model's potential value in practical applications was explored. This study provides more scientific and efficient decision support for traffic safety management and is expected to play a crucial role in fields such as traffic management and intelligent driving.