In　this　paper,　by　making　use　of　recurrent　neural　networks　(RNNs),　a　novel　event-based　robust　optimal　controller　is　designed　for　a　category　of　continuous-time　(CT)　nonlinear　nonaffine　systems　with　disturbances.　For　tackling　the　optimal-robust　problem　transformation　and　the　performance　guarantee,　a　system　identifier　is　developed　to　reconstruct　the　system,　and　an　event-triggered　critic-only　strategy　is　investigated　to　replace　the　traditional　time-based　actor-critic　structure.　The　RNN　is　adopted　to　reconstruct　the　nonaffine　system　as　an　identifier　for　obtaining　the　affine　model.　It　simplifies　the　system　structure　and　reduces　computational　burdens.　In　addition,　the　stability　of　the　unknown　nominal　system　learned　by　the　RNN　is　proved.　Then,　using　the　obtained　system　identifier　and　the　adaptive　critic　learning　structure,　the　proposed　event-triggered　robust　control　with　performance　guarantee　is　realized.　At　last,　the　effectiveness　of　the　given　design　method　is　verified　by　two　simulation　examples.