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Postgraduate students face various academic, personal, and social stressors that increase their risk of anxiety, depression, and suicide. Identifying cost-effective methods of detecting and intervening before stress turns into severe problems is crucial. However, existing stress detection methods typically rely on psychological scales or devices, which can be complex and expensive. Therefore, we propose a BERT-fused model for rapidly and automatically detecting postgraduate students’ psychological stress via social media. First, we construct an improved BERT-LDA feature extraction algorithm to extract group stress features from large-scale and complex social media data. Then, we integrate the BiLSTM-CRF named entity recognition model to construct a multi-dimensional psychological stress profile and analyze the fine-grained feature representation under the fusion of multi-dimensional features. Experimental results demonstrate that the proposed model outperforms traditional models such as BiLSTM, achieving an accuracy of 92.55%, a recall of 93.47%, and an F1-score of 92.18%, with F1-scores exceeding 89% for all three types of entities. This research provides both theoretical and practical foundations for universities or institutions to conduct fine-grained perception and intervention for postgraduate students’ psychological stress.