(untitled) (Informatica, 2024)
Under the background of globalization, tourism has been widely concerned because of its remarkable promotion to economic development. With the rapid development of information technology, especially artificial intelligence (AI), the study of tourism consumption behavior has entered a new stage. This study is devoted to exploring how to use artificial intelligence technologies such as machine learning and natural language processing to identify tourism consumption behavior and analyze its influencing factors, in order to provide accurate market positioning and product promotion strategies for the tourism industry. Through in-depth analysis of online consumer behavior data and social media comments, this study uses random forest and logistic regression analysis methods to identify and analyze the behavior characteristics and preferences of tourism consumers. It is found that specific consumption behaviors and preferences are significantly related to consumers’ personal characteristics and social and cultural background. In addition, the model evaluation results reveal the effectiveness and complementarity of random forest and logistic regression in tourism consumption behavior identification. This study fills the gap in the existing research on intelligent identification of tourism consumption behavior, and also provides data-driven decision support for tourism, thus promoting the sustainable development of tourism.