Aiming　at　the　nonlinear,　unequal　length　of　the　data　in　the　batch　processes,　a　multi-way　entropy　partial　least　squares　(MKEPLS)　process　monitoring　and　quality　prediction　method　was　proposed　combining　with　kernel　partial　least　squares　and　kernel　entropy　analysis.　The　method　solved　the　unequal　length　and　missing　data　problem　by　expanding　the　original　three-dimensional　data　in　a　new　unfolding　way,　and　the　nonlinear　character　of　the　data　could　also　be　solved　through　the　kernel　mapping　process,　which　mapped　the　data　from　low　dimensional　input　space　into　a　high　dimensional　feature　space　to　achieve　the　nonlinear　relationship　between　variables　linear　transformation.　Then,　the　data　dimensionality　reduction　was　conducted　according　to　the　kernel　entropy　eigenvalues　and　eigenvectors,　which　made　up　the　shortcomings　in　MKPLS　method.　Moreover,　the　kernel　feature　extraction　algorithm　was　introduced　to　reduce　the　computational　kernel　space　to　enable　the　online　applications　of　MKEPLS.　The　numerical　examples　and　practical　industrial　process　data　performance　show　that　MKEPLS　method　can　monitor　the　fault　effectively,　improve　the　fault　alarm　rate,　and　predict　the　quality　of　the　final　product　at　the　same　time.