In　this　paper,　we　study　the　learning　representation　of　entities　and　relationships　in　the　link　prediction　task　of　knowledge　graph.　The　knowledge　graph　is　a　collection　of　factual　triples,　but　most　of　them　are　incomplete.　At　present,　some　models　use　complex　rotation　to　model　triples,　and　obtain　more　effective　results.　However,　such　models　generally　use　specific　structures　to　learn　the　representation　of　entities　or　relationships,　and　do　not　make　full　use　of　the　context　information　of　entities　and　relations.　In　addition,　in　order　to　achieve　high　performance,　models　often　need　larger　embedding　dimensions　and　more　epoches,　which　will　cause　large　time　and　space　cost.　To　systematically　tackle　these　problems,　we　develop　a　novel　knowledge　graph　embedding　method,　named　CAQuatE.　We　propose　two　concepts　to　select　valuable　context　information,　then　design　a　context　information　encoder　to　enhance　the　original　embedding,　and　finally　use　quaternion　multiplication　to　model　triples.　The　experiment　and　results　on　two　common　benchmark　datasets　show　that　CAQuatE　can　significantly　outperform　the　existing　state-of-the-art　model　in　the　knowledge　graph　completion　task　by　obtaining　lower　dimensional　representation　vectors　with　fewer　epoches　and　no　additional　parameters.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.