This research presents a comprehensive framework for constructing and applying student portraits in personalized education through advanced data analytics and knowledge graph technology. The study addresses critical gaps in current educational data mining by proposing a multidimensional approach that integrates academic performance, learning behaviors, social interactions, emotional states, and socio-economic backgrounds into dynamic student profiles.

The theoretical foundation combines principles from user profiling, semantic networks, and affective computing to create a holistic representation of learners. Unlike traditional models that focus solely on academic metrics, this framework incorporates temporal analysis through LSTM networks to capture evolving learning patterns and social network analysis to quantify peer influence dynamics. The technical implementation leverages multimodal data fusion, combining structured academic records with unstructured behavioral logs and socio-emotional assessments through Apache Kafka for real-time processing and Neo4j for knowledge graph storage.

Key innovations include the development of lightweight adaptive engines that balance computational efficiency with model accuracy, and the integration of NLP techniques for sentiment analysis of student-generated text. The system's visualization layer, built on Streamlit, transforms complex data into actionable insights through radar charts, heatmaps, and predictive dashboards. Practical applications demonstrate significant impact in three domains: early identification of at-risk students through academic performance prediction (achieving 85% accuracy in pilot studies), optimization of resource allocation for disadvantaged learners, and personalized pathway design for STEM talent development at institutions like Harbin Institute of Technology.

Policy implications highlight the need for digital infrastructure investment and teacher training programs, with case studies showing how the system bridges China’s industry-education gap by aligning student competencies with enterprise requirements. Ethical considerations are addressed through federated learning architectures and differential privacy mechanisms that protect sensitive data while maintaining analytical precision.

The study concludes that student portrait technology represents a paradigm shift from static assessment to dynamic, predictive analytics in education. Future directions include expanding emotion recognition capabilities through multimodal sensors and developing cross-cultural adaptation frameworks for global deployment. This research establishes a new standard for data-driven education that balances personalization with equity, offering both theoretical and practical contributions to the field of precision education.
DOI：10.1007/s44366-025-0055-x