Data　mining,　especially　the　extraction　of　the　relationship　between　genes　and　proteins,　plays　an　important　role　in　the　biomedical　field.　Several　related　models　have　been　proposed　for　data　mining　in　the　biomedical　domain.　Furthermore,　manually　curated　biomedical　knowledge　bases,　which　could　assist　the　task,　have　been　used　to　enhance　the　data-mining　model.　However,　due　to　the　limitation　of　methods,　much　prior　knowledge　information　is　not　be　fully　exploited.　In　this　work,　we　propose　a　novel　method　that　reasonably　applied　the　curated　prior　knowledge　for　biomedical　text　mining　by　dual　sentence　representation　models;　one　model　is　for　the　experimental　data　and　the　other　one　is　for　the　prior　knowledge　information　sentence.　We　evaluated　our　method　on　two　community-supported　datasets;　BioNLP　and　BioCreative　corpora.　The　experimental　results　demonstrate　that　the　dual　sentence　representation　model　can　successfully　utilize　external　prior　knowledge　information　to　extract　relationship　from　biomedical　text.　Our　method　can　achieve　state-of-art　results　and　it　could　be　an　application　of　biomedical　relation　extraction　in　the　future.　©　2020　IEEE.