In　order　to　achieve　the　goal　of　intelligent　management　of　wastewater　treatment　plants,　it　is　necessary　to　monitor　the　parameters　of　wastewater　treatment　in　real　time.　Currently,　soft　measurement　technology　based　on　neural　network　is　widely　used　to　solve　the　problem　of　key　parameters　that　are　difficult　to　obtain　directly　in　wastewater　treatment　processes.　Selecting　appropriate　input　variables　can　reduce　the　complexity　of　the　neural　network　topology　while　improving　the　prediction　accuracy　of　the　soft　measurement　model.　To　this　end,　this　paper　proposes　a　variable　selection　method　based　on　the　filter-wrapped　hybrid　framework　(FWHVS),　which　uses　the　maximum　information　coefficient　(MIC)　index　and　a　radial　basis　function　(RBF)　neural　network.　The　proposed　method　only　considers　removing　redundant　and　irrelevant　variables　to　optimize　the　review　stage　of　the　SFFS　method,　thus　improving　computational　efficiency.　Finally,　common　variable　selection　methods　are　used　as　comparison　methods　to　verity　the　effectiveness　of　the　proposed　method　using　the　UCI　wastewater　treatment　dataset.