In　this　article,　an　event-based　near-optimal　tracking　control　algorithm　is　developed　for　a　class　of　nonaffine　systems.　First,　in　order　to　gain　the　tracking　control　strategy,　the　costate　function　is　established　through　the　iterative　dual　heuristic　dynamic　programming　(DHP)　algorithm.　Then,　the　event-based　control　method　is　employed　to　improve　the　utilization　efficiency　of　resources　and　ensure　that　the　closed-loop　system　has　an　excellent　control　performance.　Meanwhile,　the　input-to-state　stability　(ISS)　is　proven　for　the　event-based　tracking　plant.　In　addition,　three　kinds　of　neural　networks　are　used　in　the　event-based　DHP　algorithm,　which　aims　to　identify　the　nonaffine　nonlinear　system,　estimate　the　costate　function,　and　approximate　the　tracking　control　law.　Finally,　a　numerical　experimental　simulation　is　conducted　to　verify　the　effectiveness　of　the　proposed　scheme.　Moreover,　in　order　to　further　validate　the　feasibility,　the　algorithm　is　applied　to　the　wastewater　treatment　plant　to　effectively　control　the　concentrations　of　dissolved　oxygen　and　nitrate　nitrogen.