Conventional　Knowledge　Graph　Completion　(KGC)　methods　typically　map　entities　and　relations　to　a　unified　space　through　the　shared　mapping　matrix,　and　then　interact　with　entities　and　relations　to　infer　the　missing　items　in　the　knowledge　graph.　Although　this　shared　mapping　matrix　considers　the　suitability　of　all　triplets,　it　neglects　the　specificity　of　each　triplet.　To　solve　this　problem,　we　dynamically　learn　one　information　distributor　for　each　triplet　to　exchange　its　specific　information.　In　this　paper,　we　propose　a　novel　Triplet　Distributor　Network　(TDN)　for　the　knowledge　graph　completion　task.　Specifically,　we　adaptively　learn　one　Triplet　Distributor　(TD)　for　each　triplet　to　assist　the　interaction　between　the　entity　and　relation.　Furthermore,　on　the　basis　of　TD,　we　creatively　design　the　information　exchange　layer　to　dynamically　propagate　the　information　of　the　entity　and　relation,　thus　mutually　enhancing　entity　and　relation　representations.　Except　for　several　commonly-used　knowledge　graph　datasets,　we　still　implement　the　link　prediction　task　on　the　social-relational　and　medical　datasets　to　test　the　proposed　method.　Experimental　results　demonstrate　that　the　proposed　method　performs　better　than　existing　state-of-the-art　KGC　methods.　The　source　codes　of　this　paper　are　available　at　https://github.com/TDN　for　Knowledge　Graph　Completion.git.