Surface　electromyographic　(sEMG)　signal　contains　abundant　information　such　as　joint　torque　and　joint　motion,　which　is　widely　used　in　human-computer　interactive　intelligent　rehabilitation　equipment.　In　this　work,　the　ankle　torque　of　lower　limb　is　taken　as　the　research　object,　and　the　feature　parameters　of　sEMG　which　represent　the　fatigue　state　are　analysed.　Advance　prediction　of　fatigue　features　for　specific　time　periods　was　performed　using　a　normalized　minimum　average　square　(NLMS)　filter.　While　the　modified　cerebellar　model　neural　network　(WFCMNN)　is　used　to　classify　fatigue,　which　can　be　divided　into　three　states,　namely　no　fatigue,　transition　to　fatigue,　and　fatigue.　The　results　show　that　the　accuracy　of　classification　is　96.429%,　which　is　better　than　other　advanced　models.　At　the　same　time,　sEMG　signal　is　used　to　predict　fatigue　in　advance,　which　can　solve　the　problem　of　differences　between　different　individuals.　Such　strategy　is　helpful　for　doctors　and　physiotherapists　to　carry　out　rehabilitation　treatment　for　patients,　as　a　pre　judgment　and　diagnosis　index.　©　Content　from　this　work　may　be　used　under　the　terms　of　the　Creative　Commons　Attribution　3.0　Licence.