Previous　studies　on　batch　microbial　fermentation　usually　considered　data　maximization　but　lack　of　data　cluster　structure　information.　A　Multi-way　Kernel　Entropy　Component　Analysis　(MKECA)　method　was　proposed　to　solve　this　problem,　which　overcome　the　drawbacks　of　traditional　monitoring　methods　on　high　monitoring　failure　rates.　The　AT　method　was　first　used　for　historical　data　preprocessing　and　mapping　data　from　low-dimensional　space　to　high　dimensional　feature　space　to　solve　data　nonlinearity.　Data　in　the　high　dimensional　feature　space　was　moved　to　lower　dimension　based　on　the　size　of　the　data　kernel　entropy,　in　order　to　keep　the　original　data　distribution.　Meanwhile,　the　proposed　method　was　equivalent　to　the　traditional　method　under　certain　conditions.　Penicillin　simulation　data　verifies　that　MKECA　is　more　reliable　and　accurate　which　may　have　broad　potential　applications.　©,　2015,　Zhejiang　University.　All　right　reserved.