Research on User Behavior Analysis and Growth Strategies in Digital Exhibitions Based on Pattern Recognition

This paper explores the analysis of user behavior and the development of growth strategies in digital exhibitions using pattern recognition technology. With the rapid advancement of digitalization, particularly in response to global events like the COVID-19 pandemic, digital exhibitions have become increasingly prevalent. By applying pattern recognition, this study identifies and classifies user behavior patterns, enabling companies to better understand user needs and optimize their marketing strategies. The research highlights the significant influence of perceived value on user engagement, decision-making processes, and overall satisfaction. Through a comprehensive case study of the Canton Fair's Digital Exhibitions, the paper demonstrates how companies can leverage behavioral insights to enhance user experience, increase conversion rates, and implement effective personalized marketing strategies. The findings underscore the potential of pattern recognition technology in achieving sustainable user growth and offer actionable recommendations for its application in digital exhibitions.