<p>Personalized learning resource recommendation aims to provide learners with appropriate learning resources to alleviate information overload caused by the explosive growth of data on online learning platforms. Current research predominantly utilizes student interaction data to enhance the quality of recommendations. However, this approach neglects the intricate dependency networks among learning resources, which directly influence the effectiveness of knowledge acquisition pathways, and lacks the capacity to model individual learning abilities and objectives. To address these limitations, this study introduces a unified learning resource recommendation method (ULRRM), which integrates multidimensional graph information and employs conceptual graphs as an intermediary framework to unify resource representations across varying levels of granularity. Specifically, a resource dependency graph is established to guide resource-dependent learning through conceptual dependency relationships, thereby encoding the topological constraints of resources. Then, a local–global dual view is constructed using session history to capture both short-term behavioral patterns and the evolution of long-term interests, thereby enabling the recommendation of learning resource sequences that incorporate multidimensional graph information. Extensive experiments conducted on real datasets demonstrate that the proposed ULRRM method surpasses baseline approaches across several widely recognized evaluation metrics.</p>