To　solve　the　hysteresis　caused　by　using　torque　sensors　to　control　muscle　force　training　equipment,　a　joint　torque　prediction　model　based　on　a　group　of　antagonistic　surface　electromyography　(sEMG)　is　designed　in　this　article.　Firstly,　the　rehabilitation　training　equipment　is　built　to　provide　conditions　for　signal　acquisition　and　experimental　verification.　sEMG　is　preprocessed　and　the　variance　characteristic　of　sEMG　signal　is　selected　as　the　neural　network　input.　In　addition,　a　dynamic　recurrent　neural　network　with　the　nonlinear　auto-regressive　model　with　exogenous　inputs　(NARX)　is　used　in　this　study.　A　multi-step　ahead　prediction　model　(MSA)　based　on　the　actual　values　of　joint　moments　and　another　model　based　on　model　prediction　output　(MPO)　are　developed　respectively.　The　torque　prediction　performance　of　MSA　and　MPO　models　is　compared　by　isotonic　and　isometric　test　experiments.　Experimental　results　show　that　there　is　a　strong　correlation　between　the　predicted　output　value　and　the　actual　output　value　of　the　two　models　(Pearson　correlation　coefficient　is　greater　than　0.95).　As　the　number　of　advance　prediction　steps　increases,　the　prediction　accuracy　of　MSA　model　decreases.　However,　the　advance　prediction　time　increases.　When　n　is　less　than　29　and　35,　the　prediction　accuracy　of　MSA　is　significantly　higher　than　that　of　MPO　(p　©　2022,　Science　Press.　All　right　reserved.