Scientific　guidance　of　disease　Q&A　for　the　elderly　is　of　distinct　significance　for　improving　the　health　level　of　the　elderly,　which　is　the　immediate　need　of　the　current　aging　society.　The　traditional　question　answering　system　for　geriatric　diseases　based　on　knowledge　graph　relies　heavily　on　manual　annotation　and　has　some　problems　such　as　difficulty　in　knowledge　fusion.　The　rapid　development　of　large　models　provides　a　new　opportunity　for　geriatric　disease　Q&A　system,　but　it　also　has　problems　such　as　interpretability,　applicability　and　multi-round　Q&A　illusion.　In　this　paper,　we　propose　a　large　language　model　ElderQA-GPT　for　online　Q&A　of　geriatric　diseases　based　on　BGE　semantic　vector　knowledge　base　and　LangChain　architecture.　First,　in　order　to　improve　the　interpretability　of　Q&A　dialogues,　this　paper　adopts　the　SELF-QA　framework　and　combines　the　current　professional　websites　of　geriatric　diseases　(including　Dr.　Ding　Xiang,　Seeking　Medical　Care,　and　the　Chinese　government　website)　to　construct　a　vector　database　of　geriatric　diseases　based　on　the　BGE　semantic　vector　model;　secondly,　in　order　to　improve　the　accuracy　and　applicability　of　the　system,　the　lightweight　open　source　ChatGLM3　model　is　privately　deployed　based　on　the　LangChain　architecture,　which　enhances　the　retrieval　capability　of　the　localized　inference　knowledge　base;　lastly,　in　order　to　better　accumulate　and　comprehend　the　information　in　the　complex　contexts　and　multiple　rounds　of　dialogues,　and　to　maintain　the　semantic　consistency,　a　LoRA　fine-tuning　technique　is　used　to　enhance　the　ElderQA-GPT　multi-round　dialogue　capability.　Two　sets　of　quantitative　experiments　were　conducted　to　explore　two　dimensions:　the　interpretability　of　model　responses　and　the　resolution　of　the　illusion　problem　in　multi-round　Q&A　sessions.　Additionally,　an　ablation　study　was　carried　out　by　omitting　the　knowledge　base　to　comparatively　evaluate　the　impact　on　the　system’s　performance.　The　validation　results　demonstrate　that　ElderQA-GPT　exhibits　improved　answer　interpretability　and　applicability,　effectively　maintaining　semantic　consistency　across　multi-round　dialogues　without　introducing　false　information.　The　findings　of　this　study　significantly　advance　the　field　of　online　Q&A　for　geriatric　diseases　by　addressing　critical　challenges　such　as　interpretability,　accuracy,　and　multi-round　dialogue.　Our　proposed　model　ElderQA-GPT　not　only　improves　public　knowledge　and　understanding　of　geriatric　diseases　but　also　optimizes　medical　resource　allocation　and　enhances　health　management　for　the　elderly　population.　By　providing　interpretable　and　accurate　responses　across　multi-round　dialogues,　ElderQA-GPT　ensures　that　users　receive　reliable　medical　information　tailored　to　their　needs.　These　advancements　have　profound　implications　for　healthcare　delivery　and　society,　paving　the　way　for　more　efficient　and　effective　healthcare　services　for　elderly　individuals.　©　2024　Copyright　held　by　the　owner/author(s).