With　the　deepening　of　aging,　chronic　diseases　of　the　elderly　are　the　main　burden　of　disease　in　most　countries　in　the　world.　The　prevalence　of　chronic　diseases　in　urban　areas　in　China　is　as　high　as　75%.　Many　elderly　people　use　multiple　drugs　for　a　long　time.　Home　self-medication　problems　occur　frequently.　In　order　to　alleviate　this　problem　to　a　certain　extent,　knowledge　graph　technology　and　a　deep　learning　model　are　used　to　design　a　home　self-medication　question-answering　system　for　the　elderly　and　their　caregivers.　Explore　a　feasible　way　of　providing　automated　online　consultation　intelligent　services.　In　this　paper,　we　have　collected　medication　as　well　as　professional　Q&A　(question　and　answer)　data　in　the　field　of　aging　health,　and　constructed　a　knowledge　graph　that　meets　the　characteristics　of　medication　use　in　the　elderly.　Based　on　the　matching　rules　in　the　question　judging　module,　the　problems　entered　by　users　are　classified.　For　professional　knowledge　related　to　diseases　and　medications　of　the　elderly,　the　question-answering　system　uses　the　knowledge　graph　to　search　for　answers.　For　other　basic　knowledge　related　to　elderly　health,　the　system　uses　the　BERT　model　to　vectorize　its　users’　questions,　then　matches　the　questions　by　calculating　cosine　similarity,　thus　finding　the　corresponding　answers.　The　system　adds　the　Seq2Seq　model　as　a　supplement　to　the　answer　retrieval　method　of　the　knowledge　graph.　The　testing　results　shows　that　the　system　provides　online　consultation　services　more　accurately　and　efficiently　for　home　self-medication　for　the　elderly　and　their　caregivers.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd　2023.