Vibrating　flip-flow　screens　are　widely　employed　in　the　deep　screening　processes　of　coal　washing,　solid　waste　treatment,　metallurgy,　and　other　fields,　playing　a　crucial　role　in　enhancing　product　quality　and　production　efficiency.　The　screen　surface　and　material　movement　of　vibrating　flip-flow　screens　are　highly　complex,　and　there　is　currently　insufficient　understanding　of　their　screening　mechanism,　limiting　further　optimization　and　application.　In　this　paper,　the　Discrete　Element　Method　(DEM),　Finite　Element　Method　(FEM),　and　Multi-Body　Dynamics　(MBD)　were　integrated　to　establish　a　numerical　coupling　model　for　vibrating　flip-flow　screens,　considering　material　loads,　screen　surface　deformation,　and　screen　machine　dynamics.　The　Response　Surface　Method　was　utilized　to　analyze　the　significant　impact　of　relative　amplitude,　tension　amount,　amplitude　of　driving　screen　frame,　vibration　frequency,　and　screen　surface　inclination　on　screening　efficiency　and　material　velocity.　The　results　indicate　that　the　most　significant　factor　influencing　the　screening　of　flip-flow　screens　is　the　screen　surface　inclination.　Based　on　a　BP　neural　network,　a　five-degree-of-freedom　inclination　surrogate　model　for　flip-flow　screens　was　established.　The　whale　algorithm　was　employed　for　multi-objective　optimization　of　the　surrogate　model,　resulting　in　a　screen　surface　inclination　distribution　that　meets　the　requirements　of　different　operating　conditions.