In　this　paper,　an　event-based　iterative　adaptive　critic　algorithm　is　designed　to　address　optimal　tracking　control　for　a　class　of　nonaffine　zero-sum　games.　The　steady　control　of　the　reference　trajectory　is　obtained　by　numerical　calculation.　Then,　the　optimal　tracking　control　problem　of　unknown　nonlinear　zero-sum　games　is　transformed　into　the　optimal　regulation　problem　of　corresponding　error　dynamics.　In　order　to　ensure　that　the　closed-loop　system　possesses　favourable　control　performance　while　can　effectively　improve　the　resource　utilization,　an　appropriate　event-triggering　condition　is　introduced　to　obtain　the　tracking　policy　pair　aperiodically.　According　to　the　designed　triggering　condition　and　the　Lyapunov　stability　theory,　the　error　system　is　proved　to　be　asymptotically　stable.　In　addition,　four　neural　networks　are　constructed　to　promote　the　implementation　of　the　proposed　algorithm.　In　order　to　improve　the　accuracy　of　the　steady　control　in　target　trajectory,　the　model　network　is　used　to　approach　the　unknown　system　function　directly　instead　of　the　error　dynamic　system.　The　critic　network,　the　action　network,　and　the　disturbance　network　are　constructed　to　obtain　the　approximate　iterative　cost　function　and　the　approximate　iterative　tracking　policy　pair.　Finally,　two　examples　are　presented　to　demonstrate　the　feasibility　and　effectiveness　of　the　proposed　algorithm.　©　2023　Science　Press.　All　rights　reserved.