Judicial efficiency is critical to social stability. However, in many countries worldwide, grassroots courts face substantial case backlogs, and judicial decisions remain heavily dependent on judges' cognitive efforts, with insufficient intelligent tools to enhance efficiency. To address this issue, we propose a highly efficient law article recommendation approach combining a Knowledge Graph (KG) and a Large Language Model (LLM). First, we construct a Case-Enhanced Law Article Knowledge Graph (CLAKG) to store current law articles, historical case information, and their interconnections, alongside an LLM-based automated construction method. Building on this, we propose a closed-loop law article recommendation framework integrating graph embedding-based retrieval and KG-grounded LLM reasoning. Experiments on judgment documents from China Judgments Online demonstrate that our method boosts law article recommendation accuracy from 0.549 to 0.694, outperforming strong baselines significantly. To support reproducibility and future research, all source code and processed datasets are publicly available on GitHub (see Data Availability Statement).

翻译：司法效率对社会稳定至关重要。然而，全球许多国家的基层法院面临大量案件积压，司法判决仍高度依赖法官的认知努力，缺乏有效的智能工具来提升效率。为解决此问题，我们提出一种结合知识图谱与大语言模型的高效法律条文推荐方法。首先，我们构建了一个案例增强型法律条文知识图谱，用于存储现行法律条文、历史案例信息及其关联关系，并提出了基于大语言模型的自动化构建方法。在此基础上，我们提出了一个融合基于图嵌入的检索与基于知识图谱的大语言模型推理的闭环法律条文推荐框架。在中国裁判文书网判决文书上的实验表明，我们的方法将法律条文推荐准确率从0.549提升至0.694，显著优于现有基线方法。为支持可复现性与未来研究，所有源代码及处理后的数据集已在GitHub上公开（详见数据可用性声明）。