Dioxin　(DXN)　is　a　persistent　organic　pollutant　produced　from　municipal　solid　waste　incineration　(MSWI)　processes.　It　is　a　crucial　environmental　indicator　to　minimize　emission　concentration　by　using　optimization　control,　but　it　is　difficult　to　monitor　in　real　time.　Aiming　at　online　soft-sensing　of　DXN　emission,　a　novel　fuzzy　tree　broad　learning　system　(FTBLS)　is　proposed,　which　includes　offline　training　and　online　measurement.　In　the　offline　training　part,　weighted　k-means　is　presented　to　construct　a　typical　sample　pool　for　reduced　learning　costs　of　offline　and　online　phases.　Moreover,　the　novel　FTBLS,　which　contains　a　feature　mapping　layer,　enhance　layer,　and　increment　layer,　by　replacing　the　fuzzy　decision　tree　with　neurons　applied　to　construct　the　offline　model.　In　the　online　measurement　part,　recursive　principal　component　analysis　is　used　to　monitor　the　time-varying　characteristic　of　the　MSWI　process.　To　measure　DXN　emission,　offline　FTBLS　is　reused　for　normal　samples;　for　drift　samples,　fast　incremental　learning　is　used　for　online　updates.　A　DXN　data　from　the　actual　MSWI　process　is　employed　to　prove　the　usefulness　of　FTBLS,　where　the　RMSE　of　training　and　testing　data　are　0.0099　and　0.0216,　respectively.　This　result　shows　that　FTBLS　can　effectively　realize　DXN　online　prediction.