Recently,　the　Broad　Learning　System　(BLS)　has　been　widely　applied　to　process　monitoring,　achieved　impressive　performance.　Since　its　own　incremental　property,　BLS　can　efficiently　train　and　update　model　while　new　data　arriving.　However,　BLS　and　its　variants　ignored　the　time　dynamics　when　monitored　the　process.　To　tackle　the　above　problems,　various　methods　have　been　proposed　by　researches　to　obtain　the　hidden　time　correlation　in　data.　Slow　feature　analysis　is　an　unsupervised　algorithm,　which　is　used　to　learn　the　time　correlation　representation　of　process　monitoring.　In　this　paper,　a　method　considering　the　process　characteristics　and　time　dynamics　of　batch　process　is　proposed.　For　one,　the　slowly　changed　features　are　obtained　through　the　constructed　feature　extraction　model.　For　other,　effectively　deal　with　the　non-Gaussian　in　the　data,　built　the　global　model　to　monitor　the　whole　process.　The　performance　superiority　of　the　proposed　method　is　verified　on　the　penicillin　simulation　platform.