Knowledge graphs often suffer from sparsity and incompleteness. Knowledge graph reasoning is an effective way to address these issues. Unlike static knowledge graph reasoning, which is invariant over time, dynamic knowledge graph reasoning is more challenging due to its temporal nature. In essence, within each time step in a dynamic knowledge graph, there exists structural dependencies among entities and relations, whereas between adjacent time steps, there exists temporal continuity. Based on these structural and temporal characteristics, we propose a model named “DKGR-DR” to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs. In addition, we construct a static attribute graph to represent entities’ inherent properties. DKGR-DR is capable of modeling both dynamic and static aspects of entities, enabling effective entity prediction and relation prediction. We conduct experiments on ICEWS05-15, ICEWS18, and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance.