A psychological education model integrating artificial intelligence-based BERT in physical education

To address the limitations of traditional physical education (PE) approaches that rely heavily on manual observation for psychological assessment, this study integrates Natural Language Processing techniques with sports psychology education. A psychological education model incorporating the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model was developed to enable intelligent and precise evaluation of students’ psychological states. First, a text sentiment analysis model was constructed to extract emotional features. The BERT model was then fine-tuned for sports-specific contexts to capture deep semantic features of the text. Together, these components formed a four-dimensional feature extraction framework—semantic, emotional, thematic, and psychological. Experiments were conducted on two publicly available datasets, Emotional First Aid Dataset 1 and Psy-Insight 2. The proposed BERT-LDA-Psych model achieved average accuracy, precision, recall, and F1 scores of 91.5%, 91.0%, 90.3%, and 90.6%, respectively, on sentiment analysis tasks. It also outperformed mainstream ensemble models in psychological state recognition and feature fusion efficiency. The proposed model exhibits stable performance on general psychological counseling text corpora, establishing a reference computational framework for subsequent psychological state recognition tasks within PE contexts. The current results indicate functional feasibility in controlled textual environments; however, transferability to authentic PE classroom discourse remains uncertain.