Research on Tourism Demand Forecasting and Tourist Search Behavior Based on Internet Search Index

DOI

10.2991/978-2-38476-297-2_93How to use a DOI?

Keywords

Internet Search Index; Tourism Demand Forecasting; Tourist Search Behavior; Big Data Analysis

Abstract

This study explores the application of internet search indexes in tourism demand forecasting and analyzes the characteristics and influencing factors of tourist search behavior. Through time series analysis, regression analysis, and clustering analysis, a tourism demand forecasting model is constructed, revealing the significant impacts of seasonal factors, holiday effects, and unexpected events on tourist search behavior. The results indicate that the LSTM model excels in capturing tourism search trends, and tourist search behavior exhibits clear seasonal variations and trend fluctuations. Clustering analysis identifies three distinct groups of tourists with different search behavior characteristics, providing a basis for market segmentation and precise marketing. Specific recommendations for optimizing marketing strategies, real-time monitoring of market demand, and responding to unexpected events are proposed. Future research should further explore multi-dimensional data integration and more predictive models to enhance forecasting accuracy and application scope.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ke Ma
PY  - 2024
DA  - 2024/10/31
TI  - Research on Tourism Demand Forecasting and Tourist Search Behavior Based on Internet Search Index
BT  - Proceedings of the 2024 8th International Seminar on Education, Management and Social Sciences (ISEMSS 2024)
PB  - Atlantis Press
SP  - 763
EP  - 771
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-297-2_93
DO  - 10.2991/978-2-38476-297-2_93
ID  - Ma2024
ER  -