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.