With　the　development　and　progress　of　society,　people　are　facing　increasing　pressure.　The　emergence　of　this　phenomenon　has　led　to　a　rapid　increase　in　the　incidence　of　mental　illness.　In　order　to　deal　with　this　phenomenon,　this　paper　proposes　a　system　of　question　and　answering　on　the　basic　knowledge　of　mental　health　(MHQ&A)　by　using　deep　learning　retrieval　technology　and　knowledge　graph　technology.　The　system　MHQ&A　is　designed　mainly　for　the　general　public,　to　answer　the　basic　knowledge　of　mental　health,　especially　the　field　of　depression.　First　of　all,　the　basic　and　the　professional　question　and　answer　data　about　mental　health　were　respectively　obtained　by　the　reptilian　bot　from　the　＇IASK＇website　knowledge　and　the　＇Dr.　Dingxiang＇website.　Then,　the　questions　and　answers　obtained　through　the　crawler　are　made　into　a　Question　and　Answering　Knowledge　Graph　of　Basic　Health　Knowledge　in　the　mental　health　field,　which　is　combined　with　semantic　data　of　antidepressants　and　the　semantic　data　of　depression　papers.　Finally,　a　set　of　template　matching　rules　is　designed　to　determine　the　type　of　problem　of　users.　If　the　questions　are　about　the　professional　knowledge　of　medicine　or　thesis,　the　reasoning　template　will　be　used　to　reason　and　search　the　answer　in　the　＇Question　and　Answering　Knowledge　Graph　of　Basic　Health　Knowledge　in　the　Mental　Health　Field＇.　If　the　questions　are　about　other　basic　knowledge　in　the　field　of　mental　health,　the　BERT　model　is　used　to　vectorize　the　questions　of　users,　and　the　matching　questions　and　corresponding　answers　in　the　MHQ&A　are　found　through　cosine　similarity　calculation.　Through　the　test　of　system　accuracy,　it　is　proved　that　the　system　can　effectively　combine　deep　learning　technology　and　knowledge.　©　2021　ACM.