传统云分类方法具有主观性强、分类效率和精度较低等缺点。针对上述问题，提出一种基于轻量级梯度提升机（light gradient boosting machine, LightGBM）的云分类预测模型。首先将毫米波雷达获取到的云顶高度、云底高度、云层厚度、平均反射率因子、液态水含量、持续时间作为特征变量，结合分类标签制作为满足模型需求的数据集。之后使用此数据集构建分类模型，将云分为St、Sc、Cu、As、Ac、Cs、Cc其7类云型。结果表明，模型准确率、精确率、召回率和F₁高达94.70%、94.68%、94.97%、94.65%，较其他模型有更好的分类效果。因此，LightGBM模型对云体分类识别效果显著，具有较强适用性，对云类识别业务自动化工作有广阔的应用前景。

Traditional cloud classification methods exhibit limitations such as subjectivity and low efficiency and accuracy. To address these issues, a cloud classification prediction model based on the light gradient boosting machine (LightGBM) was proposed. Firstly, feature variables, including cloud top height, cloud bottom height, cloud layer thickness, average reflectivity factor, liquid water content, and duration obtained through millimeter-wave radar were utilized. A dataset was then constructed by combining these features with classification labels to meet the requirements of the model. This dataset was subsequently used to build a classification model that categorizes clouds into seven types: St, Sc, Cu, As, Ac, Cs, and Cc. The experimental results demonstrate that the model achieves an accuracy of 94.70%, precision of 94.68%, recall of 94.97%, and F₁ of 94.65%. These results indicate superior classification performance compared to other models. Therefore, the constructed LightGBM model shows significant effectiveness in cloud classification and recognition, exhibits strong applicability, and holds promising prospects for the automation of cloud recognition services.