The　aim　of　Medical　Knowledge　Graph　Completion　is　to　automatically　predict　one　of　three　parts　(head　entity,　relationship,　and　tail　entity)　in　RDF　triples　from　medical　data,　mainly　text　data.　Following　their　introduction,　the　use　of　pretrained　language　models,　such　as　Word2vec,　BERT,　and　XLNET,　to　complete　Medical　Knowledge　Graphs　has　become　a　popular　research　topic.　The　existing　work　focuses　mainly　on　relationship　completion　and　has　rarely　solved　entities　and　related　triples.　In　this　paper,　a　framework　to　predict　RDF　triples　for　Medical　Knowledge　Graphs　based　on　word　embeddings　(named　PTMKG-WE)　is　proposed,　for　the　specific　use　for　the　completion　of　entities　and　triples.　The　framework　first　formalizes　existing　samples　for　a　given　relationship　from　the　Medical　Knowledge　Graph　as　prior　knowledge.　Second,　it　trains　word　embeddings　from　big　medical　data　according　to　prior　knowledge　through　Word2vec.　Third,　it　can　acquire　candidate　triples　from　word　embeddings　based　on　analogies　from　existing　samples.　In　this　framework,　the　paper　proposes　two　strategies　to　improve　the　relation　features.　One　is　used　to　refine　the　relational　semantics　by　clustering　existing　triple　samples.　Another　is　used　to　accurately　embed　the　expression　of　the　relationship　through　means　of　existing　samples.　These　two　strategies　can　be　used　separately　(called　PTMKG-WE-C　and　PTMKG-WE-M,　respectively),　and　can　also　be　superimposed　(called　PTMKG-WE-C-M)　in　the　framework.　Finally,　in　the　current　study,　PubMed　data　and　the　National　Drug　File-Reference　Terminology　(NDF-RT)　were　collected,　and　a　series　of　experiments　was　conducted.　The　experimental　results　show　that　the　framework　proposed　in　this　paper　and　the　two　improvement　strategies　can　be　used　to　predict　new　triples　for　Medical　Knowledge　Graphs,　when　medical　data　are　sufficiently　abundant　and　the　Knowledge　Graph　has　appropriate　prior　knowledge.　The　two　strategies　designed　to　improve　the　relation　features　have　a　significant　effect　on　the　lifting　precision,　and　the　superposition　effect　becomes　more　obvious.　Another　conclusion　is　that,　under　the　same　parameter　setting,　the　semantic　precision　of　word　embedding　can　be　improved　by　extending　the　breadth　and　depth　of　data,　and　the　precision　of　the　prediction　framework　in　this　paper　can　be　further　improved　in　most　cases.　Thus,　collecting　and　training　big　medical　data　is　a　viable　method　to　learn　more　useful　knowledge.　©　2022　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.