Optimization Strategies for Intelligent Book Reading Based on User Behavior Analysis

Abstract

This study addresses the issues of user attention fragmentation and cognitive mismatch caused by static content presentation in digital reading platforms, proposing intelligent book reading optimization strategies grounded in user behavior analysis. By capturing interaction frequency, temporal continuity, and composite behavioral data, we developed a multi-dimensional indicator model to reflect reading states and employed clustering algorithms, temporal pattern analysis, and association rule mining to decode behavioral patterns. Building on these insights, a three-tiered optimization framework was designed, integrating personalized content recommendations, adaptive interaction adjustments, and attention-sustainment mechanisms to dynamically refine content presentation formats, page-turning speed, and auxiliary tools. Experimental results demonstrate that this strategy system significantly enhances reading efficiency and user experience, offering both theoretical foundation and methodological guidance for the development of intelligent reading systems.