目的 构建脑卒中患者误吸风险预测模型，筛选最优模型，为误吸风险患者提供预测工具。 方法 系统回顾脑卒中患者误吸相关文献提炼风险因子，结合专家函询形成风险因素调查问卷；收集浙江省某三级甲等医院515例脑卒中患者数据，经单因素、多因素分析确定核心因子；采用Logistic回归、极端梯度提升、随机森林、支持向量机、人工神经网络构建模型，通过曲线下面积、Brier分数、准确度等指标及10折交叉验证评价模型。 结果 确定年龄、格拉斯哥昏迷评分、进食体位等14项误吸独立风险因子；极端梯度提升模型综合性能最优，测试集曲线下面积=0.8071、Brier分数=0.123、F1=0.5994，10折交叉验证后稳定性良好（曲线下面积=0.792）；核心预测因子为进食体位（半坐卧位）、年龄、美国国立卫生院卒中量表评分。 结论 极端梯度提升模型可精准识别脑卒中误吸高危患者，核心因子为临床干预提供明确靶点，未来需多中心外部验证提升泛化能力。

Objective To construct a prediction model for aspiration risk in stroke patients，identify the optimal model，and provide a predictive tool for patients at risk of aspiration. Methods A systematic review of literature on aspiration in stroke patients was conducted to identify risk factors，which were then incorporated into a risk factor questionnaire developed with expert consultation. Data from 515 stroke patients at a tertiary hospital in Zhejiang Province were collected. Core factors were determined through univariable and multivariable analyses. Models were constructed using logistic regression，extreme gradient boosting，random forest，support vector machine，and artificial neural network. Model performance was evaluated using area under the curve，Brier score，accuracy，and 10-fold cross-validation. Results Fourteen independent aspiration risk factors were identified，including age，Glasgow Coma Scale score，and feeding position. The extreme gradient boosting model demonstrated optimal overall performance，AUC=0.8071，Brier score=0.123，F1=0.5994 in test set，with good stability after 10-fold cross-validation（AUC=0.792）. Core predictors included feeding position（semi-recumbent），age，and NIHSS score. Conclusion Extreme gradient boosting accurately identifies stroke patients at high risk for aspiration. Core factors provide clear targets for clinical intervention. Future multicenter external validation is needed to enhance generalization capability.