This study explores integrating the Longformer model with Conditional Random Fields (CRF) for enhancing Named Entity Recognition (NER) in the domain of healthcare data processing. It specifically focuses on patient privacy information retrieval and time information normalization, utilizing the comprehensive ‘Artificial Intelligence CUP 2023: Privacy Protection and Medical Data Standardization Challenge Dataset’. This research is conducted within the context of the AI CUP 2023 competition, which is dedicated to the privacy protection and standardization of medical data. Our approach utilized the Longformer model, renowned for its effectiveness in handling extensive text sequences, and combined it with CRF to enhance entity recognition accuracy in Electronic Health Record (EHR) text notes. To tackle challenges such as lengthy texts and class distribution imbalances, we developed a specialized process for managing large-scale textual data. This involved segmenting extensive texts into manageable chunks of 4,096 characters, which allowed for more focused and efficient training. For prediction, we employed a sliding window technique to ensure seamless integration and analysis of these text segments. This strategy was crucial in accurately retrieving patient privacy information from lengthy healthcare records. Additionally, our methodology included the implementation of rule-based methods for time information normalization, further enhancing the applicability of our approach in the medical data domain. The combination of Longformer and CRF has proven effective in accurately identifying sensitive patient information and normalizing time-related data. This approach illustrates the synergy between deep learning models and traditional methods, showcasing a robust framework for enhancing the security and efficiency of healthcare data processing.