酸性矿坑水（Acid Mine Drainage, AMD）是煤炭开采过程中产生的主要环境问题之一，对生态系统和人类健康构成严重威胁。该研究选取福建龙岩林坑煤矿为研究区，运用自组织映射（Self-Organizing Map, SOM）技术对研究区AMD污染特征进行系统的聚类分析，旨在准确评估该区域AMD污染现状并有效识别主要污染源。本研究利用SOM方法对37个水样的4种水质指标pH、Fe、Mn和SO₄^2-）进行综合分析，最终将水样划分为4个不同污染程度的聚类，明确揭示了不同污染程度水体的空间分布特征。各聚类按污染程度从高到低顺序，依次为聚类Ⅳ、聚类Ⅲ、聚类Ⅱ和聚类Ⅰ，其中聚类Ⅲ和聚类Ⅳ的样本表现出严重的污染特征，主要位于煤矿涌水点和煤矸石堆放区附近，是未来治理工作的重点；聚类Ⅱ的样本主要受污染水流汇入的影响；聚类Ⅰ包含样本量最多，表明该区域大部分水体受AMD影响程度有限。SOM方法为AMD污染特征的评估提供了有效工具，具有推广至其他煤矿区的应用潜力。未来研究需增加监测频率，以捕捉季节性变化对水质的影响。随着监测数据量的不断增加，SOM方法的应用潜力将更加明显。

Acid Mine Drainage (AMD) is one of the major environmental issues arising from coal mining, posing serious threats to ecosystems and human health. This study selected the Linkeng Coal Mine area in Longyan, Fujian as a case study and employed Self-Organizing Maps (SOM) technology to systematically analyze the pollution characteristics of AMD, aiming to accurately assess the current AMD pollution status and effectively identify the main pollution sources. The study comprehensively analyzed four water quality indicators including pH, Fe, Mn, and SO₄^2- of 37 water samples using the SOM method, and finally divided the samples into four clusters with varying degrees of pollution, clearly revealing the spatial distribution characteristics of water bodies with different levels of pollution. The clusters, in order from the highest to the lowest pollution levels, are Cluster IV, Cluster III, Cluster II, and Cluster I. Samples in clusters III and IV show severe pollution characteristics, mainly located near coal mine water inrush points and coal waste stone stacking areas, which are key areas for future remediation efforts. Samples in cluster II are primarily affected by the influx of polluted water; cluster I contains the most samples, indicating that most of the water bodies in the area are minimally affected by AMD. The SOM method offers an effective tool for assessing AMD pollution characteristics and has the potential to be applied in other coal mine areas. Future studies need to increase monitoring frequency to capture the impact of seasonal changes on water quality. As the monitoring data continues to increase, the application potential of the SOM method will become more evident.