[Objective] In order to protect private information in clinical texts and effectively identify protected health information (PHI) from unstructured structured texts, an automated scheme for removing private information from clinical records using a BiLSTM-CRF model is proposed. [Method] The discharge summary from the Electronic health records of a health information platform was selected as experimental data. According to the 18 PHI regulations specified by HIPAA combined with the characteristics of the experimental data, 7 PHI categories and 15 PHI types were determined. The BiLSTM-CRF model is used to effectively identify protected health information from unstructured clinical records. [Result] The accuracy rate, recall rate and F value of all entity category recognition were 98.66%, 99.36%, and 99.01% respectively, and the wrong labels were summarized and analyzed.. [Limitations] The optimization of model performance based on corpus characteristics needs to be improved, and the clinical text quality after automatic recognition of PHI has not been evaluated in this study. [Conclusion] The BiLSTM-CRF model realizes the automatic recognition of named entities without feature engineering, which is helpful to promote the sharing and utilization of clinical information.