Complicated　road　conditions　make　organic　Rankine　cycle　(ORC)　operation　characteristics　show　hysteresis　and　uncertainty.　Under　the　strong　coupling　correlation　of　many　operating　parameters,　how　to　realize　the　dynamic　optimization　of　ORC　comprehensive　performance　is　the　key　to　obtain　practical　application　potential.　Based　on　ensemble　learning　mechanism,　neural　network　modeling,　ensemble　system,　unsupervised　learning,　partial　mutual　information　and　optimization　algorithm　are　integrated.　This　paper　presents　a　nonlinear　time　series　prediction　and　dynamic　multi-objective　optimization　scheme.　The　average　accuracy　increased　by　at　least　59.6%.　Taking　the　thermodynamic　performance　and　environmental　impact　as　optimization　objectives,　dynamic　multiobjective　optimization　is　carried　out　under　road　conditions.　The　optimization　scheme　can　effectively　trade　off　the　nonlinear　correlation　between　thermal　efficiency　and　emissions　of　CO2　equivalent.