目前单一钻孔抽采状态评价方法通常依赖于瓦斯抽采浓度，而忽视了煤层瓦斯赋存的多样性。监督学习模型依赖于样本的特征标记，在样本量较大时，人工标注的成本较高；无监督学习模型缺乏样本标记，无法实现定性评价。针对上述问题，提出一种基于半监督学习的煤层钻孔预抽瓦斯状态评价方法。构建了包含甲烷浓度、抽采负压、环境温度等 8 项指标的多维度评价体系，采用层次分析法（AHP）与模糊评价法（FEM）结合的权重赋值方法，建立抽采效果等级划分标准。在此基础上，提出基于高斯混合模型（GMM）与 K−Means算法的半监督学习模型（SSGMM/SSK−Means），通过融合少量人工标注样本与大量未标注数据，实现单一钻孔抽采状态的动态分类。SSGMM聚集度更好，SSK−Means效率更高，形成“精度−效率”的互补关系。在陕西黄陵二号煤矿 215 工作面的应用结果表明：SSGMM和 SSK−Means的最大聚集度（MVCR）和修正 Rand 指数（ARI）分别达 82.64% 和 85.83%，显著优于传统聚类方法；通过动态反馈机制优化后，原等级为“差”的钻孔抽采效率提升 5.26%～5.80%，补差率达 100%。

Current evaluation methods for single-borehole gas extraction status typically rely on gas concentration, while overlooking the diversity of coal seam gas occurrence. Supervised learning models depend on labeled sample features, but manual labeling becomes costly when the sample size is large. Unsupervised learning models lack sample labeling, making qualitative evaluation infeasible. To address these issues, an evaluation method based on semi-supervised learning was proposed for the gas pre-extraction status evaluation of coal seam boreholes. A multi-dimensional evaluation system was established, incorporating eight indicators such as methane concentration, extraction negative pressure, and ambient temperature. The weighting method combining the analytic hierarchy process (AHP) and fuzzy evaluation method (FEM) was used to establish classification standards for extraction performance. Building on this, a semi-supervised learning model based on the Gaussian mixture model (GMM) and K-Means algorithm (SSGMM/SSK-Means) was developed. By integrating a small number of manually labeled samples and a large quantity of unlabeled data, the model enabled dynamic classification of single-borehole extraction status. The SSGMM demonstrated better clustering rate, while the SSK-Means achieved higher efficiency, developing a complementary "accuracy-efficiency" relationship. The application results from the 215 working face of the Huangling No. 2 Coal Mine in Shaanxi Province showed that the maximum validity clustering rate (MVCR) and adjusted rand index (ARI) of SSGMM and SSK-Means reached 82.64% and 85.83%, respectively, significantly outperforming conventional clustering methods. After optimization through a dynamic feedback mechanism, boreholes initially classified as "poor" showed an improvement of 5.26% to 5.80% in extraction efficiency, achieving a 100% remediation rate.