In　this　paper,　a　two-step　phase　partitioning　strategy　is　proposed.　Firstly,　the　number　of　phases　is　automatically　determined　according　to　the　intra-class　and　inter-class　similarity　of　feature　space　data,　thus　avoiding　excessive　manual　intervention.　Secondly,　the　phases　are　partitioned　by　step-wise　adding　the　kernel　entropy　extended　load　matrix　(KEELM),　avoiding　the　wrong　division　of　phases　caused　by　unstable　state　of　working　condition　conversion.　A　process　monitoring　model　based　on　multiway　kernel　entropy　independent　component　analysis　(MKEICA)　is　constructed　in　each　sub-phase　to　deal　with　complex　batch　processes　with　nonlinear　and　non-Gaussian　properties.　A　new　statistics　index　based　on　the　idea　of　high　order　cumulant　analysis　(HCA)　is　constructed　in　each　sub-phase　for　process　monitoring.　Compared　with　the　traditional　second-order　statistics,　it　can　obtain　high-order　statistical　information.　Finally,　the　proposed　method　is　applied　to　the　penicillin　simulation　platform　process　and　compared　with　the　traditional　multiway　kernel　independent　components　analysis　(MKICA)　and　HCA　methods　to　verify　the　effectiveness　of　the　method　that　is　mentioned　above.