利用1959—2021年内蒙古人工观测冰雹记录，分析冰雹分布的时空特征，并基于机器学习算法构建了冰雹识别方法。结果表明：（1） 时间分布上，冰雹事件出现的站数和站日数均呈现下降趋势；空间分布上，冰雹多集中在阴山山脉和大兴安岭一带，冰雹多发区沿山脉伸展分布。（2） 冰雹发生具有明显的季节变化和日变化特征，每年5—9月是冰雹频发月份，占全年雹日的91.79%，雹日中12:00—19:00是冰雹的多发时段。（3） 利用随机森林、LightGBM、K近邻和决策树4种机器学习算法，通过数据预处理、预报因子选择、模型训练、模型调优等步骤，对内蒙古冰雹天气过程进行建模与评估。评估结果表明，采用机器学习方法可以有效地识别冰雹天气过程，各模型的TS评分均达到0.83以上，命中率达到92%以上，随机森林算法在测试集上识别效果最优。研究结果可为内蒙古冰雹预报预警和人工防雹工作提供参考。

Based on the manual observation of hail records in Inner Mongolia, China, from 1959 to 2021, the spatial and temporal characteristics of hail distribution are analyzed, and a hail recognition method is constructed based on machine learning algorithms. The results are as follows: (1) Regarding temporal distribution, the number of hail days and affected stations in Inner Mongolia shows a decreasing trend. In terms of spatial distribution, hail events are predominantly concentrated in the Yinshan Mountains and the Greater Hinggan Mountains, with hail-prone areas extending along these mountain ranges. (2) Hail exhibits distinct seasonal and diurnal characteristics. The peak hail months in Inner Mongolia are from May to September, accounting for 91.79% of the annual hail days. The most frequent period for hail occurrences is between 12:00 BST and 19:00 BST. (3) Four machine learning algorithms (random forest, LightGBM, K-proximity, and decision tree) are used to model and evaluate hail events in Inner Mongolia through data preprocessing, predictor selection, model training, and tuning. Verification results indicate that machine learning methods effectively identify hail events, with the threat score of each model exceeding 0.83 and hit rates surpassing 92%. Among these, the random forest algorithm demonstrates the best recognition performance on the test set. These findings provide useful references for hail forecasting and artificial hail prevention in Inner Mongolia.