Hard　classification　for　multistage　fermentation　process　and　cause　of　the　defects　of　false　alarm　and　alarm　failure,　in　order　to　effectively　reduce　the　omission　and　the　rate　of　false　positives,　this　paper　proposes　a　strategy　based　on　extended　nuclear　entropy　load　matrix.　First,　the　three-mention　training　data　array　of　fermentation　process　is　unfolded　in　batch　ways,　resulting　in　two-dimension　forms.　Then,　kernel　entropy　component　analysis(KECA)was　done　for　each　time　slice　matrix　to　obtain　its　load　matrix.　After　that,　time　slice　matrix　was　added　to　the　nuclear　load　matrix　of　entropy,　and　the　change　of　the　nuclear　load　matrix　of　entropy　was　utilized　to　describe　the　changes　of　batch　processes.　The　KECA　monitoring　model　was　established　at　each　stage　of　the　division　after　the　stage　of　nuclear　load　matrix　of　entropy　was　determined　by　FCM　algorithm.　At　last,　the　effectiveness　and　utility　of　the　proposed　method　were　validated　through　the　simulation　of　fed-batch　penicillin　and　E.coli　production　of　interleukin-2.Results　showed,　the　proposed　method　could　not　only　divide　the　stage　and　reduce　the　false　alarm　precisely,　but　also　detect　the　production　difficulty　more　advance.　©　2018,　Editorial　Department　of　Control　Theory　&　Applications　South　China　University　of　Technology.　All　right　reserved.