Dioxin　is　a　trace　organic　pollutant　emitted　from　municipal　solid　waste　incineration　process.　Limited　by　the　complexity　and　high　cost　of　relative　technology,　the　big　time　delay　of　dioxin　emission　concentration　detection　has　become　one　of　the　key　factors　restricting　the　optimize　control　of　municipal　solid　waste　incineration　process.　Although　the　data-driven　soft　sensing　model　with　the　characteristics　of　low　cost,　fast　response　and　high　precision　can　effectively　solve　the　above　problems,　the　dioxin　modeling　method　must　fit　the　small　sample　and　high-dimensional　characteristics　of　the　modeling　data.　In　this　paper,　a　broad　hybrid　forest　regression　soft　sensing　method　is　proposed,　which　consists　of　feature　mapping　layer,　latent　feature　extraction　layer,　feature　enhancement　layer　and　incremental　learning　layer.　Firstly,　a　hybrid　forest　group　composed　of　random　forest　and　completely　random　forest　is　constructed　for　high-dimensional　feature　mapping.　Secondly,　the　latent　features　extraction　of　the　fully　connected　mixed　matrix　is　carried　out　according　to　the　contribution　rate,　and　the　information　measurement　criterion　is　used　to　ensure　the　maximum　transmission　and　minimize　redundancy　of　potential　valuable　information.　Thus,　the　model　complexity　and　computational　consumption　are　reduced.　Then,　the　feature　enhancement　layer　is　trained　based　on　the　extracted　potential　information　to　enhance　the　feature　representation　ability.　Finally,　the　incremental　learning　layer　is　constructed　by　using　incremental　learning　strategy,　and　the　weight　matrix　is　obtained　by　using　the　Moore-Penrose　pseudo　inverse.　The　experimental　results　on　high-dimensional　benchmark　and　dioxin　datasets　of　municipal　solid　waste　incineration　process　show　the　effectiveness　and　superiority　of　the　proposed　method.　©　2023　Science　Press.　All　rights　reserved.