<p>In response to the intricate and non-standardized nature of e-commerce product descriptions, we propose an enhanced BERT-BiLSTM-CRF entity extraction model to address the limitations of existing models in accurately extracting necessary entities. BERT serves as the encoder, BiLSTM constructs the feature extractor, and CRF refines the model for improved accuracy. The e-commerce product text data are preprocessed and annotated according to real-world task requirements, and the model’s performance is validated on both self-generated and MSRA public datasets. Experimental results show that the BERT-BiLSTM-CRF model achieves an F1 score of 98% and precision of 98% on the self-generated dataset, effectively catering to real-world e-commerce product information mining tasks. Furthermore, it achieves a relative balance between precision and recall, enabling more precise extraction of essential entities. The model’s performance improvements are attributed to the integration of BERT’s contextual understanding, BiLSTM’s ability to capture long-range dependencies, and CRF’s label sequence optimization, which collectively enhance the model’s generalization and accuracy across diverse datasets. Compared to traditional models, the proposed BERT-BiLSTM-CRF model shows a 6.5% improvement in F1 score on the MSRA dataset and a 4% improvement in precision on the self-generated dataset, highlighting its superior capability in handling complex and non-standardized e-commerce text data.</p>