Admittance　control,　benefiting　from　human-robot　interaction　compliance,　is　widely　used　in　rehabilitation　robot　studies.　However,　using　inappropriate　parameters　for　the　admittance　control　model　can　cause　harm　like　overuse　syndrome.　Therefore,　it　is　necessary　to　dynamically　adjust　these　parameters　to　assist　patients　under　varying　recovery　periods,　enabling　active　rehabilitation　training　across　a　wider　range　of　recovery　stages.　Integrating　multiple　intelligent　approaches　presents　a　promising　solution　to　this　challenge.　This　paper　proposes　a　variable　admittance　control　strategy　that　employs　a　variable　operator　fuzzy　neural　network　(VAC-VOFNN).　The　VOFNN　facilitates　the　fuzzification　of　the　inference　process,　leading　to　superior　non-linear　fitting　capability.　Additionally,　the　network＇s　parameters　are　updated　online　to　match　the　rehabilitation　stages　of　different　subjects.　Compared　to　the　admittance　control　strategy　with　fixed　parameters　(ACS-FP)　and　the　variable　admittance　control　strategy　with　fuzzy　neural　networks　(VAC-FNN),　the　proposed　strategy　reduces　the　root　mean　square　(RMS)　of　surface　electromyography　(sEMG)　from　the　medial　gastrocnemius　by　29.14%　and　29.04%,　respectively,　while　also　decreasing　the　average　interaction　torque　by　28.63%　and　12.24%,　respectively.　These　results　suggest　that　the　proposed　strategy　leads　to　reduced　effort　from　subjects　and　increased　training　cycles　before　muscle　fatigue　during　the　same　rehabilitation　activities.　This　makes　it　beneficial　for　ankle　rehabilitation　of　patients　in　different　recovery　periods.