In　this　paper,　a　novel　nonlinear　method　named　multiway　Laplacian　autoencoder　(MLAE)　is　proposed　for　batch　process　monitoring.　Autoencoder　(AE)　is　an　effective　unsupervised　learning　neural　network　for　nonlinear　feature　extraction.　Compared　with　traditional　AEs,　the　proposed　method　has　two　main　advantages.　Firstly,　traditional　AEs　usually　ignore　the　local　structure　of　the　original　dataset.　The　proposed　MLAE　method　integrates　graph　Laplacian　regularization　to　the　loss　function,　and,　thus,　the　local　structure　of　the　normal　process　data　is　fully　considered.　Secondly,　the　Laplacian　matrix　of　the　regularization　term　is　constructed　by　an　average　local　affinity　matrix　of　all　batch　runs,　which　contains　the　information　of　the　stochastic　deviations　among　batches.　Furthermore,　two　statistics,　ie,　H-2　and　SPE　statistics,　are　developed　based　on　the　extracted　hidden　representation　and　the　retained　reconstruction　error.　The　effectiveness　and　advantages　of　the　MLAE-based　monitoring　strategy　are　illustrated　by　a　benchmark　penicillin　fermentation　process　and　a　real　E.　coli　fermentation　process.