Insight Analysis of Tourists' Behavior and Preference by Deep Learning in Tourism Destination Management

With the rapid development of tourism, the application of deep learning technology in tourism destination management has gradually attracted widespread attention. The purpose of this study is to explore the potential value of deep learning in the insight analysis of tourists' behavior and preferences, so as to provide better decision support for tourism destination management. By analyzing the massive data left by tourists in social media, mobile applications and online comments, we adopt deep learning methods, including neural network and natural language processing technology, to extract key information about tourists' behaviors and preferences. Research shows that deep learning technology can effectively gain insight into tourists' behavior, including travel preferences, activity selection and time arrangement; It helps to identify tourists' interest points and hot spots, and predict future tourism trends; Deep learning can also provide personalized travel advice and improve tourists' satisfaction and loyalty. This study provides a powerful tool for tourism destination managers, helping them to better understand tourists' needs, plan resources, improve the attraction of destinations, and thus promote the sustainable development of tourism.