As　a　pivotal　role　in　the　control,　optimization,　and　monitoring　of　contemporary　industrial　processes,　soft　sensors　are　frequently　employed　in　the　prediction　of　key　quality　variables.　To　achieve　accurate　prediction　of　key　quality　variables　in　industrial　processes,　a　soft　sensor　modeling　method　based　on　the　self-organizing　fuzzy　neural　network　with　the　clustering,　merging,　and　splitting　scheme　(SOFNN-CMS)　is　proposed.　First,　the　supervised　fuzzy　C-means　clustering　algorithm　is　proposed　to　identify　the　appropriate　initial　center　and　width　of　the　fuzzy　neural　network,　obtaining　appropriate　initial　fuzzy　rules.　Then,　a　neuron　merging　and　splitting　strategy　is　designed　to　adjust　the　structure　of　the　fuzzy　neural　network,　by　merging　and　splitting　the　hidden　neurons　according　to　the　distance　of　clusters,　increasing　the　adaptability　of　the　fuzzy　neural　network.　Besides,　to　accelerate　the　convergence　of　estimation　errors,　an　improved　Levenberg　Marquardt　algorithm　is　utilized　to　update　neural　network　parameters　in　the　training　phase,　realizing　the　soft　sensor　modeling　of　key　quality　variables.　The　effectiveness　of　the　proposed　SOFNN-CMS　neural　network　is　demonstrated　on　two　benchmark　problems　and　an　industrial　debutanizer　column.　Finally,　the　experiments　showcase　that　the　proposed　SOFNN-CMS　neural　network　can　obtain　better　soft　sensor　modeling　performance　with　a　compact　structure.