Automatic　Knowledge　Graph　Completion　is　becoming　the　main　research　direction　of　Knowledge　graph　construction.　Among,　the　entity　prediction　method　can　complete　the　complement　of　entities　in　RDF　triples,　and　is　widely　used　in　the　generation　process　of　the　Knowledge　graph.　In　order　to　complete　the　Chinese　medical　Knowledge　graph,　this　paper　proposes　an　entity　prediction　method　based　on　BRRT　sentence　embedding　and　classification.　This　method　needs　three　steps,　the　first　step　is　to　introduce　a　large-scale　medical　corpus　to　fine　tune　the　basic　BERT　model　into　a　BERT　Domain　model.　The　second　step　is　obtaining　the　sentence　embedding　through　the　model　for　candidate　triples.　The　third　step　is　to　obtain　the　top　N　candidate　entity　lists　according　to　the　ranking　of　classifier　probabilities　of　all　candidate.　In　order　to　verify　the　effectiveness　of　this　method,　a　series　of　experiments　are　conducted　on　the　BIOS.　The　experimental　results　show　that　the　optimal　accuracy　of　the　entity　prediction　method　in　this　paper　is　20.5%,　which　is　7.2%　higher　than　that　using　Word　embedding+distance.