随着向新型能源体系的转型加速，亟待开展对多元负荷用户的复杂用能特性分析的深入研究。提出了一种综合考量电、冷、热多元负荷耦合特性的用户用能特性标签库构建技术及用户画像方法。首先运用快速相关性滤波算法剔除高冗余低相关特征，并通过随机森林和递归式特征消除算法精选出具有强区分能力的用能特征。在聚类阶段，改进的自适应三支密度峰值聚类算法（three-way adaptive density peak clustering, 3W-ADPC）通过结合自适应近邻搜索和三支聚类算法提升负荷聚类效果。实证结果表明，所提方法具备在计算效率和聚类精度上的双重优势，能够精准揭示多元负荷用户综合用能特性和深层次信息，证实所提方法在多元负荷用户行为研究中的实用价值。

With the acceleration of the transition to new energy systems, it is urgent to carry out in-depth research on the complex energy characteristics of multi-load users. A technology of constructing user energy characteristic label library and a user portrait method were proposed, which comprehensively considered the coupling characteristics of electric, cold and thermal multiple loads. Firstly, the high redundancy and low correlation features were eliminated by the fast correlation filtering algorithm, and the features with strong distinguishing ability were selected by the random forest and recursive feature elimination algorithm. In the clustering stage, the improved three-way adaptive density peak clustering (3W-ADPC) algorithm improved the load clustering effect by combining the adaptive neighbor search and the three-branch clustering algorithm. The empirical results show that the proposed method has dual advantages in computational efficiency and clustering accuracy, and can accurately reveal the comprehensive energy use characteristics and deep information of multi-load users, which confirms the practical value of the proposed method in the study of multi-load users' behavior.