To　approach　the　brain-like　neural　network　and　further　improve　the　performance　of　the　modular　neural　network　(MNN),　an　adaptive　evolutionary　modular　neural　network　with　intermodule　connections　(EA-ICMNN)　is　proposed　in　this　study.　The　EA-ICMNN　is　composed　of　a　group　of　multilayer　neural　networks.　Unlike　traditional　MNNs,　in　addition　to　the　intramodule　connections　of　subnetworks,　intermodule　connections　are　built　for　EA-ICMNN.　All　the　parameters　of　the　EA-ICMNN　are　learned　by　the　improved　Levenberg-Marquardt　algorithm,　and　the　optimal　structure　is　adaptively　determined　by　the　improved　mutation　operator　in　the　multiobjective　optimization　algorithm　NSGAII.　To　verify　the　effectiveness　of　the　proposed　model,　the　EA-ICMNN　is　tested　on　several　benchmark　datasets　and　a　practical　prediction　problem　for　biochemical　oxygen　demand　in　wastewater　treatment　process.　The　experimental　results　show　that　the　proposed　model　has　better　generalization　ability　than　other　MNNs　and　that　its　structure　is　simplified　by　its　sparse　intermodule　connections.