Tour Guide Robot Based on Large Language Model and User Behavior Analysis

With the continuous development of human-computer interaction technology, the applications of tour guide robots in scenarios such as museums and exhibition halls are becoming increasingly widespread. However, traditional tour guide robots primarily rely on pre-defined content and texts, as well as pre-planned routes, making it difficult to adjust in real-time based on the diverse needs and interests of different visitors. This limitation restricts interactivity and engagement. To address this issue, this paper proposes an intelligent tour guide robot system that integrates large language model and user behavior analysis technology, aimed at dynamically adjusting tour content by real-time perception of visitors' interests. This system leverages the natural language processing capabilities of large language model, along with user behavior analysis techniques, to provide visitors with a more personalized and interactive tour experience. Experimental validation shows that the proposed tour guide robot system can enhance the accuracy and proactivity of tour presentations, thereby increasing user engagement and the overall effectiveness of the tour.