A　knowledge　graph　is　a　repository　that　represents　a　vast　amount　of　information　in　the　form　of　triplets.　In　the　training　process　of　completing　the　knowledge　graph,　the　knowledge　graph　only　contains　positive　examples,　which　makes　reliable　link　prediction　difficult,　especially　in　the　setting　of　complex　relations.　At　the　same　time,　current　techniques　that　rely　on　distance　models　encapsulate　entities　within　Euclidean　space,　limiting　their　ability　to　depict　nuanced　relationships　and　failing　to　capture　their　semantic　importance.　This　research　offers　a　unique　strategy　based　on　Gibbs　sampling　and　connection　embedding　to　improve　the　model＇s　competency　in　handling　link　prediction　within　complex　relationships.　Gibbs　sampling　is　initially　used　to　obtain　high-quality　negative　samples.　Following　that,　the　triplet　entities　are　mapped　onto　a　hyperplane　defined　by　the　connection.　This　procedure　produces　complicated　relationship　embeddings　loaded　with　semantic　information.　Through　metric　learning,　this　process　produces　complex　relationship　embeddings　imbued　with　semantic　meaning.　Finally,　the　method＇s　effectiveness　is　demonstrated　on　three　link　prediction　benchmark　datasets　FB15k-237,　WN11RR　and　FB15k.