This study proposes a learner profile framework based on multi-feature fusion, aiming to enhance the precision of personalized learning recommendations by integrating learners’ static attributes (e.g., demographic data and historical academic performance) with dynamic behavioral patterns (e.g., real-time interactions and evolving interests over time). The research employs Term Frequency-Inverse Document Frequency (TF-IDF) for semantic feature extraction, integrates the Analytic Hierarchy Process (AHP) for feature weighting, and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’ interests. Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’ behaviors and provides context-aware learning resource recommendations. The study introduces a novel paradigm for learner modeling in educational technology, combining methodological innovation with a scalable technical architecture, thereby laying a foundation for the development of adaptive learning systems.