Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. Using a deep learning approach, this research studied the framework in forecasting monthly Macau tourist arrival volumes. The empirical results demonstrated that the deep learning approach significantly outperforms support vector regression and artificial neural network models. Moreover, the construction and identification of highly relevant features from the proposed deep network architecture provide practitioners with a means of understanding the relationships between various tourist demand forecasting factors and tourist arrival volumes. This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field •A deep learning method is presented to forecast tourist demand.•The introduced method represents an automated approach to feature engineering.•The method overcomes the linearity limitations of existing lag order detection.•The case study on Macau confirms the superior performance of the proposed approach.•The introduced method can be applied to different tourism destinations.