In　this　paper,　the　adaptive　fuzzy　fixed-time　bipartite　consensus　tracking　control　problem　is　studied　for　stochastic　nonlinear　multi-agent　systems　(MASs)　with　unknown　control　gains　and　time-varying　output　constraints.　Firstly,　in　order　to　address　the　difficulties　arising　from　the　unknown　control　gains,　the　Nussbaum　technique　is　employed.　In　the　meantime,　the　tan-type　nonlinear　mapping　(NM)　function　is　introduced　which　guarantees　the　predefined　output　constraints　are　not　violated.　Then,　different　from　the　previous　control　strategies　in　which　they　only　focused　on　the　balanced　directed　topology,　the　classification　optimization　algorithm　(COA)　is　presented　to　accomplish　the　bipartite　consensus　tracking　control　according　to　the　structurally　unbalanced　directed　topology.　Besides,　by　combining　the　adaptive　backstepping　technique　with　the　adding　power　integration　methodology,　the　nonsingular　fixed-time　control　strategy　is　proposed.　The　proposed　adaptive　fuzzy　fixed-time　control　strategy　ensures　that　the　bipartite　consensus　tracking　errors　converge　to　a　region　near　zero　in　fixed　time　and　all　the　signals　in　the　closed-loop　system　are　bounded　in　probability.　Lastly,　the　effectiveness　of　the　presented　control　scheme　is　demonstrated　with　a　simulation　example.　　©　1993-2012　IEEE.