Traditional prediction methods of tropical cyclone tracks are mainly statistical forecasting models and numerical prediction models [3]. Numerical models require a powerful computational capability to deal with complicated thermodynamic formulas and to simulate the internal structure of a tropical cyclone [4]. With the development of computer technology and the establishment of monitoring stations, the numerical model has been widely used, but it still has the problems of relatively high computational complexity and low prediction accuracy. For example, according to data published by the Shanghai Typhoon Institute, errors of approximately 97.4, 188.2, and 302.7 km have occurred in typhoon path prediction during 24, 48, and 72 h time windows when using a numerical model, whereas such errors were only approximately 84.2, 145.6, and 205.4 km when using a subjective empirical approach [5]. Statistical models for finding tropical cyclones based on characteristics from the historical records are unable to further improve the prediction accuracy [6]. Additionally, with the establishment of ocean observation stations, ground stations, and meteorological satellites, the volume of data available is also increasing, resulting in a big data problem to predict tropical cyclone paths. Deep learning algorithms have recently been applied to image processing, natural language processing, and object detection [7; 8], showing significant strength in processing the large volume of complex data in two aspects. On one side, deep learning algorithms can conduct hidden feature extraction from a massive dataset of a number of variables and increase the generalization capability, such as using an auto-encoder (AE) network to improve the model training efficiency. On the other side, deep learning algorithms are more efficient at processing large sequential time datasets. For example, gated recurrent unit (GRU) networks can extract temporal features and make a prediction at the next timestamp. Therefore, by gradually training and adjusting the models, these deep learning algorithms can achieve optimal performance and reduce prediction error, making them appropriate to tackle the tropical cyclone track prediction problem. In this study, we propose a novel data-driven prediction approach based on deep learning algorithms. This approach contains two layers, an AE layer and a GRU layer. First, because of certain concerns regarding implicit feature extraction during the data analysis phase in most traditional statistical methods and physical simulation models, an AE network, which has been proven to achieve a good performance in extracting potential data features and reducing data volumes [9], is introduced. The AE layer aims to extract the potential and implicit deep features from a preprocessed multi-dimensional dataset for improving the prediction performance and poor generalization problems inherited in traditional deep learning models. Second, a novel customized batch method is proposed that can deal with a non-equalized tropical cyclone dataset. This is because tropical cyclones occur during different time lengths, which results in an input dataset for a GRU layer of different lengths. Furthermore, a GRU model, which is a variant of the RNN model, forms connections between layers of neurons, which is inherited from the RNN model, while also solving the long-term dependency problem in an RNN through the addition of gate structures and memory cells [10]. In general, the GRU layer can effectively handle the time-series problem and improve the prediction accuracy. Therefore, we propose a novel prediction model in which an AE layer can extract features between tropical cyclone landing locations and meteorological factors, then a GRU layer can take features from the AE layer as the input and learn temporal information to predict tropical cyclone tracks.