Temporal　knowledge　graph　completion　(TKGC)　is　an　extension　of　the　traditional　static　knowledge　graph　completion　(SKGC)　by　introducing　the　timestamp.　The　existing　TKGC　methods　generally　translate　the　original　quadruplet　to　the　form　of　the　triplet　by　integrating　the　timestamp　into　the　entity/relation,　and　then　use　SKGC　methods　to　infer　the　missing　item.　However,　such　an　integrating　operation　largely　limits　the　expressive　ability　of　temporal　information　and　ignores　the　semantic　loss　problem　due　to　the　fact　that　entities,　relations,　and　timestamps　are　located　in　different　spaces.　In　this　article,　we　propose　a　novel　TKGC　method　called　the　quadruplet　distributor　network　(QDN),　which　independently　models　the　embeddings　of　entities,　relations,　and　timestamps　in　their　specific　spaces　to　fully　capture　the　semantics　and　builds　the　QD　to　facilitate　the　information　aggregation　and　distribution　among　them.　Furthermore,　the　interaction　among　entities,　relations,　and　timestamps　is　integrated　using　a　novel　quadruplet-specific　decoder,　which　stretches　the　third-order　tensor　to　the　fourth-order　to　satisfy　the　TKGC　criterion.　Equally　important,　we　design　a　novel　temporal　regularization　that　imposes　a　smoothness　constraint　on　temporal　embeddings.　Experimental　results　show　that　the　proposed　method　outperforms　the　existing　state-of-the-art　TKGC　methods.　The　source　codes　of　this　article　are　available　at　https://github.com/QDN　for　Temporal　Knowledge　Graph　Completion.git.