The　furnace　temperature　field　is　key　information　to　reflect　the　combustion　conditions　and　is　related　to　process　safety,　equipment　failure　rates,　and　pollution　emissions　in　municipal　solid　waste　incinerators.　However,　the　measurement　of　the　actual　temperature　distribution　of　the　incinerator　is　difficult　because　of　the　limitations　of　physical　sensors　(thermocouples).　A　new　furnace　temperature　field　modeling　strategy　was　proposed　to　address　the　above　problem.　It　included　five　virtual　temperature　points,　with　the　numerical　simulation　and　the　deep　forest　regression　algorithm　used.　First,　numerical　models　of　different　benchmark　conditions　were　established　via　FLIC.　Second,　modeling　data　were　obtained　by　orthogonal　experiments.　Third,　deep　forest　regression　based　on　the　fully　connected　cross　layer　(DFR-clfc)　was　used　to　establish　five　virtual　temperature　models.　The　five　models　had　high　prediction　performance　(R2　=　0.707,　0.870,　0.918,　0.895,　and　0.907)　and　characterized　the　distribution　of　the　furnace　temperature.　The　grate　speed　had　the　greatest　influence　on　temperature　distribution,　and　based　line　condition　(2)　was　more　suitable　for　a　stable　operation.　The　novelties　of　the　work　are　as　follows.　(1)　Furnace　temperature　field　estimation　based　on　FLIC　simulation　and　data-driven　model.　(2)　Multi-conditions　design　based　on　micro,　composition,　and　operating　parameters.　(3)　Multi-dimensional　visual　analysis　of　incinerator　virtual　temperature　field.　(4)　Temperature　field　model　by　using　deep　ensemble　forest　with　potential　interpretability.