Market segmentation and demand forecasting of international tourism service trade based on CNN
DOI:
https://doi.org/10.71451/ISTAER2512Keywords:
CNN; International Tourism Service Trade; Market Segmentation; Demand Forecasting; Deep LearningAbstract
In the context of globalization and informatization, international tourism service trade has become an important part of the global economy. As changes in tourism demand are affected by multiple factors such as economic situation, policy adjustments, and consumer behavior, traditional demand forecasting methods have been unable to cope with complex market changes. Based on the convolutional neural network (CNN) model, this study segmented and forecasted the international tourism service trade market. Through multi-dimensional market data, the model can automatically extract features from the data, effectively identify the potential laws of market demand changes, and provide accurate demand forecasts. Experimental results show that the CNN model has high accuracy in predicting the macro trends of global tourism demand and demand changes in major markets. However, the model has certain errors in dealing with short-term market fluctuations and markets with slow economic recovery. To this end, future research can optimize CNN by combining other deep learning models to improve the prediction accuracy and computational efficiency of the model. The study provides a scientific basis for decision-making in the tourism industry and promotes the sustainable development of the global tourism industry.
References
[1] He, K., Ji, L., Wu, C. W. D., & Tso, K. F. G. (2021). Using SARIMA–CNN–LSTM approach to forecast daily tourism demand. Journal of Hospitality and Tourism Management, 49, 25-33. DOI: https://doi.org/10.1016/j.jhtm.2021.08.022
[2] Jung, S., Zhang, R. Y., Chen, Y., & Joe, S. (2025). Optimizing demand forecasting for business events tourism: a comparative analysis of cutting-edge models. Journal of Hospitality and Tourism Insights, 8(1), 370-390. DOI: https://doi.org/10.1108/JHTI-12-2023-0960
[3] Tolun, Ö. C., Zor, K., & Tutsoy, O. (2025). A comprehensive benchmark of machine learning-based algorithms for medium-term electric vehicle charging demand prediction. The Journal of Supercomputing, 81(3), 475. DOI: https://doi.org/10.1007/s11227-025-06975-8
[4] Du, R., He, D., Song, Y., Ding, Z., Chan, S., & Li, X. (2025). Privacy-Preserving Short-Term Travel Demand Forecasting Based on Federated Learning. IEEE Transactions on Vehicular Technology. DOI: https://doi.org/10.1109/TVT.2025.3547823
[5] Nguyen, V. H., Nguyen, N., Nguyen, T. H., Nguyen, Y. N., Dinh, M. T., & Doan, D. (2025). Customer emotion detection and analytics in hotel and tourism services using multi-label classificational models based on ensemble learning. Annals of Operations Research, 1-31. DOI: https://doi.org/10.1007/s10479-024-06434-2
[6] Steinbacher, M., Steinbacher, M., & Steinbacher, M. (2025). Using CNN to Model Stock Prices. Computational Economics, 1-42. DOI: https://doi.org/10.1007/s10614-025-10887-3
[7] Zhao, H., Zhao, P., & Jiang, S. (2025). Examining the dynamic engagement process of passengers in online car-hailing system: a view of user value. Transportation Planning and Technology, 48(2), 293-312. DOI: https://doi.org/10.1080/03081060.2024.2340640
[8] Zhao, T., Chen, G., Gatewongsa, T., & Busababodhin, P. (2025). Forecasting Agricultural Trade Based on TCN-LightGBM Models: A Data-Driven Decision. Research on World Agricultural Economy, 207-221. DOI: https://doi.org/10.36956/rwae.v6i1.1429
[9] Mou, T., & Wang, H. (2025). Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism. Scientific Reports, 15(1), 1121. DOI: https://doi.org/10.1038/s41598-025-85139-3
[10] Zaka, F., Nafisah, I. A., Lin, J., Almazah, M. M., Hussain, I., Almazroui, M., ... & Louati, H. (2025). Real-time temperature nowcasting using deep learning models across multiple locations. Modeling Earth Systems and Environment, 11(3), 165. DOI: https://doi.org/10.1007/s40808-025-02353-8
[11] Villar, J. R. N., & Lengua, M. A. C. (2025). Predicting demand in changing environments: a review on the use of reinforcement learning in forecasting models. Bulletin of Electrical Engineering and Informatics, 14(2), 1355-1370. DOI: https://doi.org/10.11591/eei.v14i2.8848
[12] Wei, C. (2025). Tourist attraction image recognition and intelligent recommendation based on deep learning. Journal of Computational Methods in Sciences and Engineering, 14727978251318805. DOI: https://doi.org/10.1177/14727978251318805
[13] Nguyen, D. T., Li, Y. M., Peng, C. L., Cho, M. Y., & Nguyen, T. P. (2024). Monthly Tourism Demand Forecasting With COVID‐19 Impact‐Based Hybrid Convolution Neural Network and Gate Recurrent Unit. International Journal of Tourism Research, 26(6), e2812. DOI: https://doi.org/10.1002/jtr.2812
[14] Núñez, J. C. S., Gómez‐Pulido, J. A., & Ramírez, R. R. (2024). Machine learning applied to tourism: A systematic review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(5), e1549. DOI: https://doi.org/10.1002/widm.1549
[15] Essien, A., & Chukwukelu, G. (2022). Deep learning in hospitality and tourism: a research framework agenda for future research. International Journal of Contemporary Hospitality Management, 34(12), 4480-4515. DOI: https://doi.org/10.1108/IJCHM-09-2021-1176
[16] Westland, J. C., Mou, J., & Yin, D. (2019). Demand cycles and market segmentation in bicycle sharing. Information Processing & Management, 56(4), 1592-1604. DOI: https://doi.org/10.1016/j.ipm.2018.09.006
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