Modelling and forecasting tourism revenue with quantile neural networks: An empirical study from China

The rapid expansion of global tourism has underscored the critical need for accurate regional tourism revenue (TR) forecasting to support sustainable economic development. This study takes Shaanxi Province, a major tourism destination in China, as a case to analyse TR trends and predict future changes. We develop a Quantile Neural Network (QNN) model, optimized through grid search and ten-fold cross-validation, which demonstrates superior predictive accuracy compared to benchmark models. To quantify prediction uncertainty, we propose the QNN-Bootstrap algorithm that combines QNN with resampling to construct confidence intervals, enhancing forecast reliability. Furthermore, the model’s generalizability is validated using tourism data from Beijing, confirming its robust performance in diverse regional contexts. To assess potential risks, this study also simulates TR dynamics under external shocks such as economic crises, pandemics, and natural disasters, and discusses the recovery trajectory following the COVID-19 pandemic. The findings provide valuable insights and policy recommendations to support evidence-based decision-making and promote resilient and sustainable tourism development. Copyright © 2025 Informa UK Limited, trading as Taylor & Francis Group.