Enterprise AI Analysis

The optimal number of clusters was determined to be four (k=4) via the Elbow Method, demonstrating a 62% reduction in total variance and signifying fundamentally distinct behavioral archetypes. These clusters were characterized as: In Depth Research Oriented, Exam Driven, General Exploration Oriented, and Low Frequency Random, each with unique engagement profiles visualized through a radar chart. The Random Forest classifier achieved perfect classification (100% accuracy, 0 false positives, 0 false negatives) across 6,040 instances, reliably distinguishing these user segments. Feature importance analysis revealed that 'active_day_ratio', 'num_days', and 'visits_per_active_day' accounted for approximately 97% of the classification power, underscoring the dominance of temporal engagement patterns and consistency as key differentiators. The entire modeling pipeline demonstrated integrity and coherence, validating the robustness of the identified behavioral patterns.

This research successfully transformed macro-level reader segments into micro-level, actionable profile labels, shifting library service decision-making from fuzzy perception to precise understanding and enabling personalized, on-demand service configuration. The findings provide empirical support and practical strategies for optimizing library collections, enhancing spatial efficiency, and developing personalized services, thereby contributing new methodological insights to smart library research. However, the study acknowledges limitations including its reliance on static data and predefined feature sets, and the lack of real-time behavioral dynamics or privacy-compliant data fusion. Future work will explore incorporating additional data dimensions like digital resource logs, seat reservation patterns, and anonymized movement trajectories to develop more dynamic, comprehensive, and ethically grounded reader profiles, ultimately advancing toward a more intelligent and human-centered library paradigm.