Human-machine　interface(HMI)　technology　has　gradually　become　a　research　hotspot　with　the　continuous　development　of　computer　technology　and　Internet　of　Things　(IOT)　technology.　Hand　gesture　recognition　(HGR)　as　an　important　part　of　HMI　technology　has　been　widely　concerned.　Among　the　many　technical　routes　of　HGR　technology,　the　wearable　HGR　technology　based　on　A-mode　ultrasound　shows　great　application　potential　due　to　its　advantages　such　as　lightweight　device,　free　from　sensors＇　constraint　etc.　However,　in　the　process　of　processing　A-mode　ultrasonic　signal,　the　amplitude　of　the　signal　at　the　main　position　may　vary　due　to　muscle　fatigue　and　strength　etc.　when　the　same　gesture　is　repeated　some　time　later.　In　this　paper,　we　design　a　method　to　overcome　this　problem　and　accomplish　HGR　in　offline　state　by　setting　the　threshold　and　use　normalization　method　for　the　energy　feature　of　ultrasonic　signal　according　to　the　threshold.　Three　machine　learning　algorithms,　including　support　vector　machine　(SVM),　linear　discriminant　analysis　(LDA)　and　Naive　Bayes　(NB)　were　used　to　verify　the　feasibility　of　this　method,　and　the　average　recognition　accuracy　in　the　experiment　reached　95.26%.　Signal　stability　is　also　verified　to　be　improved.　In　addition,　We　design　a　recognition　strategy　based　on　NB　algorithm　in　this　paper　so　that　the　model　can　identify　the　unknown　gestures　(i.e.　gestures　that　have　not　been　trained　by　the　model).　Five　known　gestures　and　five　unknown　gestures　were　set　in　the　experiment,　the　average　recognition　accuracy　of　the　known　gestures　is　91.2%,　and　the　unknown　gestures　is　81.8%.　This　method　can　be　used　to　optimize　the　user　experience　of　HGR　system.