Comparative experimental analysis.

Student well-being prediction is of great significance for promoting personalized education and preventing mental health problems, but existing methods suffer from limitations including lack of psychological theory guidance, neglect of student relationship modeling, and insufficient cross-cultural adaptability. This study proposes the PERMA-GNN-Transformer model, which innovatively integrates Seligman’s PERMA positive psychology theory with graph neural networks and Transformer architecture. The model achieves the transformation from raw educational data to psychologically meaningful representations through a theory-driven feature embedding mechanism, designs four types of student relationship graphs based on cosine similarity, Euclidean distance, learning styles, and PERMA weighting, and employs a five-head attention mechanism corresponding to the five PERMA dimensions. Experiments on the Western cultural background Lifestyle and Wellbeing Data (n = 12,757) and the East Asian cultural background International Student Mental Health Dataset (n = 268) demonstrate that compared to the optimal baseline methods, our proposed model achieves an 18.9% performance improvement on large-scale datasets and a 27.8% improvement on small-scale datasets, with the PERMA comprehensive evaluation metric reaching 0.792 and passing statistical significance tests at p