森林火灾严重威胁着人类生命和财产安全，森林火灾风险的精确预测对于减灾防灾具有重要意义。受地形、气象、植被覆盖和人类活动等因素的影响，森林火灾诱发的原因存在区域差异性。以四川省木里县历史森林火灾事件为响应变量，以地形、气象、植被覆盖和人类活动数据为解释变量，充分发挥CatBoost在高维稀疏数据和分类问题方面的优势，构建了一种基于CatBoost的高精度树林火灾预测模型。实验结果表明，相较于随机森林（random forest, RF）、极端梯度提升（extreme gradient boosting, XGBoost）和梯度提升决策树（gradient boosting decision trees, GBDT）模型，CatBoost模型不仅可获得更高的建模精度，而且树林火灾的预测精度也得到了显著改善，预测准确率达91.36%，曲线下的面积（area under curve, AUC）为0.970。基于所构建模型进行火灾预测，可为木里县森林火灾的早期防范提供参考依据。

Forest fires pose a significant threat to human lives and property. Accurate prediction of forest fire risk is crucial for disaster mitigation and prevention. Influenced by factors such as terrain, meteorology, vegetation cover, and human activities, the causes of forest fires exhibit regional differences. This study uses historical forest fire events in Muli County, Sichuan Province as the response variable, with terrain, meteorological data, vegetation cover, and human activity data as explanatory variables. Leveraging CatBoost's strengths in handling high-dimensional sparse data and classification problems, a high-precision forest fire prediction model was constructed based on CatBoost. The experimental results indicate that, compared to random forest (RF), extreme gradient boosting(XGBoost), and gradient boosting decision trees(GBDT) models, the CatBoost model achieves higher modeling accuracy and significantly improves forest fire prediction accuracy, with a prediction accuracy rate of 91.36% and an area under curve(AUC) value of 0.970. Predictions made using this model can provide valuable references for the early prevention of forest fires in Muli County.