We developed a method for updating and constructing Chinese language teaching resources by integrating sensor technology and knowledge graphs. The method addresses the challenges of a mismatch between resource supply and learner demand through a closed loop of perception–analysis–update. Wearable sensors and AI cameras were used to collect real-time, quantitative multimodal data on learners’ physiological and behavioral states, including body temperature, heart rate, and classroom interactions. These sensor data, along with learning platform data, were used to train an eXtreme Gradient Boosting model. The model achieved a prediction accuracy of 89.2% and an area under the receiver operating characteristic curve of 0.93, indicating the accurate distinction of knowledge entities that need updating and those that do not. The feature importance analysis revealed that user ratings (0.603) and recency (0.226) were the most influential factors for predicting update necessity. The knowledge graph was iteratively updated through a multistep process including pattern mining, filtering, and a final review by experts. The resulting knowledge graph incorporated nodes for new content, such as internet slang and cross-cultural variations in festivals, demonstrating the method’s ability to adapt to linguistic evolution and cultural nuances. Through the establishment of a closed-loop architecture, multimodal sensor data, including physiological photoplethysmography signals and behavioral time-of-flight imaging, are used for the expansion and weight adjustment of a domain-specific knowledge graph. This cognitive-aware update mechanism ensures that learning resources evolve along with real-time learner demands, providing a scalable blueprint for intelligent, sensor-driven knowledge management systems in various disciplines. The results of this study also underscore the role of sensor data in developing contextualized, personalized, and optimized digital learning resources that lead to a learner-centered learning environment.