In the medical field, unstructured medical text holds rich medical knowledge. Identifying medical entities in this text accurately is crucial for structured medical databases, knowledge graphs, and intelligent diagnostic systems. Medical text has unique features, making it hard for traditional NER methods to identify complex medical entities. In particular, the recognition of nested entities within medical text poses a significant challenge, as it requires systems to recognize and understand the complex hierarchical relationships between entities, placing higher demands on traditional entity recognition systems. To overcome the challenges of nested entity recognition in medical text, we propose a method that combines semantic knowledge enhancement and global pointer optimization. Initially, we incorporate semantic prior knowledge of entity categories, capturing the interplay between labels and text by integrating label relationships. This allows us to obtain candidate entity information enriched with integrated label details. Following this, we establish a classification module to evaluate and score these candidate entities along with their labels, enabling entity prediction. To address nested entities, we introduce a Efficient GlobalPointer module that computes the likelihood of each text span being a specific entity type, thus bolstering nested entity recognition. By merging the outputs from both modules, we arrive at the final predicted entities. Experimental results indicate that our method excels on two flat entity datasets, CMedQANER and CCKS2017, as well as on the nested entity dataset CMeEE. Compared to baseline models, our approach demonstrates notable performance enhancements.