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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.研究生群体面临各类学业、个人及社会压力源，这会提升其罹患焦虑症、抑郁症及自杀的风险。在压力演变为严重心理问题前，识别出兼具成本效益的检测与干预方法至关重要。然而，现有的压力检测方法通常依赖心理量表或专业设备，此类方案往往复杂度较高且成本不菲。为此，本文提出一种融合BERT的模型，可通过社交媒体快速自动检测研究生的心理压力水平。首先，本文构建一种改进的BERT-LDA（潜在狄利克雷分配，Latent Dirichlet Allocation）特征提取算法，从大规模复杂社交媒体数据中提取群体压力特征。随后，本文融合BiLSTM-CRF（双向长短期记忆网络-条件随机场）命名实体识别模型，构建多维心理压力画像，并分析多特征融合下的细粒度特征表征。实验结果表明，所提模型优于BiLSTM等传统模型，其准确率达92.55%、召回率为93.47%、F1值为92.18%，且三类实体的F1值均超过89%。本研究可为高校或相关机构开展研究生心理压力的细粒度感知与干预工作提供理论与实践依据。