The　relationship　of　biomedical　entity　is　the　cornerstone　of　acquiring　biomedical　knowledge.　It　is　of　great　significance　to　the　construction　of　related　databases　in　the　biomedical　field　and　the　management　of　medical　literature.　How　to　quickly　and　accurately　extract　the　required　relationships　of　biomedical　entity　from　massive　unstructured　literature　is　an　important　research.　In　order　to　improve　accuracy,　we　use　support　vector　machine　(SVM)　which　is　a　machine　learning　algorithm　based　on　feature　vectors　to　extract　relationships　of　entities.　We　extract　the　five　main　relationships　in　medical　literature,　including　ISA,　PART_OF,　CAUSES,　TREATS　and　DIAGNOSES.　First　of　all,　related　topics　are　used　to　search　medical　literature　from　PubMed　database,　such　as　disease-drug,　cause-disease.　These　documents　are　used　as　experimental　data　and　then　processed　to　form　a　corpus.　In　selection　of　features,　the　method　of　information　gain　is　used　to　select　the　influential　entities＇　own　features　and　entities＇　context　features.　On　this　basis,　semantic　predicates　are　added　as　a　feature　to　improve　accuracy.　The　experimental　results　show　that　the　accuracy　of　extraction　is　increased　by　5%-10%.　In　the　end,　Resource　Description　Framework　(RDF)　is　used　to　store　extracted　relationships　from　the　corresponding　documents,　and　it　provides　support　for　the　subsequent　retrieval　of　related　documents.