Research and Application of AI-Based Interactive Exhibits in Wuhan Museum of Science and Technology

Abstract

This article aims to explore the development and application of AI-based interactive exhibits in Wuhan Museum of Science and Technology. By utilizing computer vision, natural language processing, and machine learning technologies, an innovative exhibit development and application system is proposed. This system employs deep learning algorithms and data analysis methods to achieve real-time perception of visitor behavior and adaptive interaction. The development process involves designing user interfaces and interaction methods to effectively enhance visitor engagement and learning outcomes. Through evaluation and comparison in practical applications, the potential of this system in enhancing exhibit interaction, increasing visitor engagement, improving educational effectiveness, and expanding avenues for scientific knowledge dissemination are validated.