Predicting future events through temporal knowledge graph reasoning has attracted widespread attention from researchers in recent years. Since future events are unknown, a comprehensive understanding of the semantic correlations and historical evolution patterns of entities and relations is a prerequisite for accurate reasoning. However, current models frequently overlook the semantic correlations of relations and fail to consider that the impact of historical repetitive facts on the query varies over time. To tackle these issues, a novel temporal knowledge graph reasoning model based on Relation Graphs and Time-Guided Attention mechanism (RG-TGA) within a local–global framework was proposed. Specifically, RG-TGA designed a relation graph construction and aggregation method in the local information encoder to capture the structural characteristics of relations. Additionally, an improved relation-aware graph attention network was performed to aggregate the local neighbors of entities. In the global information encoder, RG-TGA adopted a time-guided attention mechanism to allocate attention weights to historical repetitive facts based on their time spans. Finally, temporal knowledge graph reasoning was executed by integrating the local and global information. Experimental results on five public datasets show that RG-TGA outperforms the state-of-the-art model by 2.63%, 3.95%, 3.49%, and 2.71% in MRR, Hits@1, Hits@3, and Hits@10, respectively.