Abstract: Knowledge graph reasoning has attracted extensive attention because of its ability to alleviate the inherent incompleteness of knowledge graphs. However, existing approaches mainly rely on embedding models or chain-based rules, but they still suffer from limitations in interpretability and expressiveness. Existing approaches mainly rely on embedding models or chain-based rules, but they still suffer from limitations in interpretability and expressiveness. Existing rule learning methods are often limited to linear dependency modeling and struggle to capture multi-path interactions and hierarchical semantic relations, while traditional rule-mining methods usually follow the “enumeration–verification” paradigm, resulting in high computational costs and insufficient modeling of deep semantic correlations among rules. To address these challenges, this paper proposes CGRL (Contrastive Graph Rule Learning), a novel method for learning graph-structured rules through contrastive learning. CGRL enhances rule learning through a threefold mechanism. First, it applies an adaptive clustering strategy to generate soft type labels for entities, introducing semantic priors that improve rule generalization. Second, it designs a multi-hop neighborhood sampling method to construct semantic subgraphs and to generate high-quality positive and negative rule instances. Third, it employs a Transformer-based encoder and a contrastive learning objective to map rule pairs into a unified embedding space, thereby jointly capturing structural dependencies and semantic consistency. Extensive experiments on six public knowledge graph completion benchmarks demonstrate that CGRL achieves substantial improvements over state-of-the-art baselines. On datasets with relatively complex relational structures, such as FB15k-237 and WN18RR, CGRL yields average gains of 6.04% in MRR and 16.26% in Hits@1, confirming its effectiveness in discovering high-quality graph-structured rules and significantly enhancing reasoning performance.