It　is　difficult　to　establish　an　accurate　mechanism　model　for　prediction　incinerator　temperatures　due　to　the　comprehensive　complexity　of　the　municipal　solid　waste　(MSW)　incineration　process.　In　this　paper,　feature　variables　of　incineration　temperature　are　selected　by　combining　with　mutual　information　(MI),　genetic　algorithms　(GAs)　and　stochastic　configuration　networks　(SCNs),　and　the　SCN-based　incinerator　temperature　model　is　obtained　simultaneously.　Firstly,　filter　feature　selection　is　realized　by　calculating　the　MI　value　between　each　feature　variable　and　the　incinerator　temperature　from　historical　data.　Secondly,　the　fitness　function　of　GAs　is　defined　by　the　root　mean　square　error　of　the　incinerator　temperature　obtained　by　training　SCNs,　and　features　obtained　by　MI　methods　are　searched　iteratively　to　complete　the　wrapper　feature　selection,　where　the　SCN-based　incinerator　temperature　prediction　model　is　obtained.　Finally,　the　proposed　model　is　verified　by　MSW　incinerator　temperature　historical　data.　The　results　show　that　the　SCN-based　prediction　model　using　the　hybrid　selection　method　can　better　predict　the　change　trend　of　incinerator　temperature,　which　proves　that　the　SCNs　has　great　development　potential　in　the　field　of　prediction　modeling.