Affected　by　the　operation　environment　and　uncertainties,　batch　processes　have　complex　dynamic　characteristics,　presenting　autocorrelation　and　mutual　correlation　among　process　variables.　Many　conventional　methods　tend　to　ignore　this　property　when　constructing　monitoring　models,　resulting　in　inadequate　process　feature　extraction　and　unsatisfactory　monitoring　performance.　A　novel　monitoring　method　named　Dynamic　Hidden　Variable　Fuzzy　Broad　Neural　Network　(DHVFBN)　monitoring　model　is　constructed　for　batch　processes　to　address　the　aforementioned　issues.　For　the　details,　in　order　to　capture　dynamic　feature　and　nonlinearity　in　batch　process,　Slow　Feature　Analysis　(SFA)　is　first　used　to　extract　the　slowly　changing　components　and　sorting　them　from　the　raw　time　series　data,　which　has　strong　capability　in　dynamic　feature　processing.　In　the　monitoring　model,　the　incremental　learning　ability　of　Fuzzy　Broad　Learning　System　(FBLS)　is　adopted　to　complete　the　quick　reconstruction　and　expeditiously　updating　of　the　monitoring　model　without　having　to　retrain　the　entire　network　when　new　fault　samples　are　added　to　the　training　set　or　the　accuracy　of　the　network　barely　meet　the　requirements,　which　hugely　relieves　the　computation　burden　and　thus　accomplishes　the　online　fault　surveillance.　In　addition,　to　fully　extract　the　feature　of　process　data,　the　fuzzy　mechanism　of　the　FBLS　is　then　selected　to　extract　the　full　fuzzified　feature　information　of　the　process　data　so　as　to　identify　the　slight　difference　between　abnormal　and　normal　process　data　effectively,　which　can　improve　the　performance　of　fault　monitoring.　Finally,　this　method　is　evaluated　by　conducting　experiments　on　the　penicillin　fermentation　platform　and　Real-world　industrial　application.　Compared　with　common　state-of-the-art　methods　involved,　the　monitoring　results　indicate　that　the　DHVFBN　outperforms　them.