Knowledge　graph　completion　(KGC)　has　been　widely　explored,　but　the　task　of　temporal　knowledge　graph　completion　(TKGC)　for　predicting　future　events　is　far　from　perfection.　Some　embedding-based　approaches　have　achieved　significant　results　on　the　TKGC　task　by　modeling　the　structural　information　of　each　temporal　snapshot　and　the　evolution　between　temporal　snapshots.　However,　due　to　the　uneven　distribution　of　data　in　knowledge　graphs　(KGs),　models　that　only　utilize　local　structure　and　time　series　information　suffer　from　information　sparsity,　resulting　in　some　entities　failing　to　obtain　a　better　embedding　representation　due　to　less　available　information.　Moreover,　existing　methods　usually　do　not　distinguish　between　the　time　span　and　frequency　of　historical　information,　which　reduces　the　performance　of　link　prediction.　For　this　reason,　we　propose　the　G　lobal　and　L　ocal　Information-A　ware　Net　work　(GL-ANet)　to　capture　both　global　and　local　information.　In　particular,　to　model　global　information,　we　capture　global　structural　information　of　entities　across　time　using　a　global　neighborhood　aggregator　to　enrich　the　representation　of　entities;　global　historical　information　is　obtained　based　on　the　frequency　and　time　span　of　historical　facts,　focusing　on　recent　and　frequent　events　rather　than　all　historical　events　to　suggest　the　performance　of　link　prediction;　to　model　local　information,　we　propose　a　two-layer　attention　network　to　capture　local　structural　information　at　each　timestamp,　using　a　gating　mechanism　and　GRU　to　capture　local　evolution　information.　Extensive　experiments　demonstrate　the　effectiveness　of　our　model,　achieving　significant　improvements　and　outperforming　state-of-the-art　models　on　five　benchmark　datasets.　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.