Temporal　Knowledge　Graph　Completion　(TKGC)　aims to　address　the　incompleteness　issue　present　in　Temporal　Knowledge Graph　(TKG).　Existing　methods　for　TKGC　mainly　fall　into　two　categories:　one　is　the　method　that　combines　temporal　information　with　entity and　relation　representations,　which　makes　it　difficult　to　deal with　complex　temporal　patterns,　and　the　other　is　the　method　that uses　Graph　Neural　Network　(GNN)　to　capture　the　neighborhood　structure,　which　usually　focuses　on　single　timestamps　and　ignores the　interactions　between　different　timestamps.　To　address these　limitations,　we　propose　a　novel　method　called　Dynamic　Periodicity　Perception　and　Multi-Graph　Integration　(DPPMI).　DPPMI　introduces　Temporal　Category　Sampling　strategy　and　Relation-Aware Graph　Transformer　module　to　effectively　capture　contextual　information　across　different　time　points.　To　handle　complex　temporal　dynamics,　we　introduce　a　novel　period　embedding　method　based　on　the　prime.　Furthermore,　we　introduce　a　specialized　attention　mechanism to　dynamically　perceive　the　significance　of　various　period　embeddings,　enabling　the　model　to　effectively　identify　and　capture　complex　temporal　patterns.　Experimental　results　show　that　our　model　improves　the　Mean　Reciprocal　Rank　(MRR)　on　the　ICEWS14,　YAGO11k, and　Wikidata12k　datasets　by　around　3.9%,　10%,　and　3.7%,　respectively,　compared　to　the　state-of-the-art　baseline.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.