To　measure　whether　the　sewage　treatment　meets　the　standards,　biochemical　oxygen　demand　(BOD5)　is　often　used　to　determine,　but　the　measurement　of　this　indicator　often　has　a　long　time　lag　and　difficult　to　observe　the　real-time　changes　of　BOD5,　which　brings　inconvenience　to　the　industrial　process.　The　soft　measurement　technology　based　on　neural　network　can　realize　BOD5　prediction　at　every　moment　by　means　of　auxiliary　variables,　which　has　attracted　people＇s　attention.　However,　there　are　still　two　problems　with　soft　measurement　technology,　neural　network-based　soft　measurement　technology　has　high　computational　complexity　and　a　certain　time　delay　in　measurement;　and　it　cannot　handle　non-Gaussian　data　well.　To　solve　them,　this　paper　introduces　an　over-complete　broad　learning　system　(OBLS)　based　on　feature　fusion　to　deal　with　the　problems　of　real-time　measurement　of　BOD5　in　sewage　treatment　industrial　process.　In　view　of　the　data　characteristics,　the　feature　extraction　ability　of　the　BLS　is　improved,　the　non-Gaussian　characteristic　of　sewage　data　is　captured　by　the　method　of　Overcomplete　Independent　Component　Analysis　(OICA),　and　the　OBLS　is　used　to　deal　with　the　real-time　soft　measurement.　Compared　with　state-of-the-art　methods　on　the　sewage　standard　test　platform,　the　measurement　accuracy　of　the　proposed　algorithm　is　found　to　be　higher　and　the　performance　is　more　stable.　(C)　2021　Published　by　Elsevier　B.V.