Prediction of Tourism Economic Development in Smart Cities Assisted by <scp>6G</scp> Wireless Networks

ABSTRACT With the development of smart cities based on 6G technology, it greatly promotes the development of the tourism industry. Deep models based on time series forecasting have been widely applied in tourism economic prediction. However, these models cannot utilize heterogeneous data from multiple sources and are unable to model the long‐range dependencies of time series. To resolve these issues, we construct an efficient variant spatiotemporal graph convolutional network for predicting the development of the tourism economy. Firstly, we collect heterogeneous data from different tourist attractions through 6G technology and construct tourist attraction graph data. Then we embed the Mamba module into the spatiotemporal graph convolutional network for long‐range dependency modeling. Moreover, a gated recurrent unit (GRU) is introduced for temporal feature extraction. On our tourism economy forecasting dataset, experimental results show that our proposed model achieves the best predictive performance.