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Land use change (LUC) exhibits obvious spatio-temporal dependency. Previous cellular automata (CA)-based methods usually treated the LUC dynamics as Markov processes and proposed a series of CA-Markov models, which however, were intrinsically unable to capture the long-term temporal dependency. Meanwhile, such models used only numerical proportion of neighboring land use (LU) types to represent neighborhood effects of LUC, which inevitably neglected the complicated spatial heterogeneity and thus caused inaccurate simulation results. To address these problems, this paper presents a novel CA model integrated with deep learning (DL) techniques to model spatio-temporal LUC dynamics. Our DL-CA model firstly uses a convolutional neural network to capture latent spatial features for complete representation of neighborhood effects. A recurrent neural network then extracts historical information of LUC from time-series land use maps. A random forest is appended as binary change predictor to avoid the imbalanced sample problem during model training. Land use data collected from 2000 to 2014 of the Dongguan City, China were used to verify our proposed DL-CA model. The input data from 2000 to 2009 were used for model training, the 2010 data for model validation, and the data collected from 2011 to 2014 were used for model evaluation. In addition, four traditional CA models of multilayer perceptron (MLP)-CA, support vector machine (SVM)-CA, logistic regression (LR)-CA and random forest (RF)-CA were also developed for accuracy comparisons. The simulation results demonstrate that the proposed DL-CA model accurately captures long-term spatio-temporal dependency for more accurate LUC prediction results. The DL-CA model raised prediction accuracy by 9.3%–11.67% in 2011–2014 in contrast to traditional CA models. • A novel CA model is integrated with deep learning to model the dynamics of LUC. • LUC is treated as a long-term process and the temporal dependency is modeled by RNN. • For the spatial dependency in LUC, CNN is built to learn the latent spatial features. • RF is used as bootstrap to avoid imbalanced sampling during CNN-RNN model training. [ABSTRACT FROM AUTHOR]