Temporal　Knowledge　Graphs　(TKGs),　represented　by　quadruples,　describe　facts　with　temporal　relevance.　Temporal　Knowledge　Graph　Completion　(TKGC)　aims　to　address　the　incompleteness　issue　of　TKGs　and　has　received　extensive　attention　in　recent　years.　Previous　approaches　treated　timestamps　as　a　single　node,　resulting　in　incomplete　parsing　of　temporal　information　and　the　inability　to　perceive　temporal　hierarchies　and　periodicity.　To　tackle　this　problem,　we　propose　a　novel　model　called　Time　Split　Network　(TSN).　Specifically,　we　employed　a　unique　approach　to　handle　temporal　information　by　splitting　timestamps.　This　allows　the　model　to　perceive　temporal　hierarchies　and　periodicity,　while　reducing　the　number　of　model　parameters.　Additionally,　we　combined　convolutional　neural　networks　with　stepwise　fusion　of　temporal　features　to　simulate　the　hierarchical　order　of　time　and　obtain　comprehensive　temporal　information.　The　experimental　results　of　entity　link　prediction　on　the　four　benchmark　datasets　demonstrate　the　superiority　of　the　TSN　model.　Specifically,　compared　to　the　state-of-the-art　baseline,　TSN　improves　the　MRR　by　approximately　2.6%　and　1.3%　on　the　ICEWS14　and　ICEWS05-15　datasets,　and　improves　the　MRR　by　approximately　33.5%　and　34.6%　on　YAGO11k　and　Wikidata12k,　respectively.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.