The　dioxin　(DXN)　emission　concentration　is　an　important　indicator　related　to　le　stability　and　harmless　operation　of　municipal　solid　waste　incinerator　(MSWI)　process,　and　also　one　of　the　key　parameters　for　MSWI　process　to　realize　optimal　operation　control.　In　the　actual　MSWI　process,　the　process　variables　such　as　furnace　temperature,　grate　speed　and　air　pressure　are　hundreds.　However,　the　modeling　samples　containing　DXN　emission　concentrations　are　small,　which　makes　it　impossible　to　establish　accurate　DXN　emission　concentration　forecasting　model.　In　this　study,　a　forecasting　model　construction　method　by　using　random　forest　(RF)-based　transfer　learning　was　proposed.　At　first,　initial　weights　are　assigned　to　the　source　domain　and　target　domain　samples.　Then,　an　RF-based　DXN　emission　concentration　transfer　learning　model　is　established,　and　the　prediction　errors　are　used　as　the　indicator　to　calculate　the　weight　adjustment　coefficients.　Finally,　the　sample　weights　are　adjusted　through　iteration　loop　in　terms　of　increase　the　source　domain　instance　weights　that　related　to　the　target　domain.　The　method　proposed　in　this　paper　can　transfer　the　source　domain　sample　to　enhance　the　prediction　performance　of　the　DXN　emission　concentration　forecasting　model.　The　experimental　results　based　on　the　actual　industrial　data　show　the　effectiveness　of　the　proposed　method.