In response to the issue of fuzzy event argument boundaries in traditional Chinese medicine (TCM) event extraction, an event extraction model integrating local and global semantic features (EE-LGSF) was proposed, which combined convolutional neural networks, bidirectional long short-term memory networks, and attention mechanisms to enhance the effectiveness of TCM event extraction. Firstly, multi-dimensional local feature information of the text was extracted by combining convolutional neural networks with different filter window sizes, while the global feature information of the text was captured using bidirectional long short-term memory networks. Secondly, on this basis, dynamic interaction between local and global information was achieved through gating mechanisms to enhance the ability of model to identify argument boundaries. Furthermore, a fuzzy span attention mechanism was introduced to dynamically adjust the attention range, thereby optimizing the decision-making process for argument spans. Finally, label prediction was performed using conditional random fields. The results indicate that the proposed model improves the F1 score by 3.0 to 11.0 percentage points on the TCM medical records data-set, demonstrating superior performance in addressing TCM event extraction issues compared to related models. The proposed model effectively leverages both local and global semantic information of the text, enhances the flexibility of span learning and improves the capability of the model to identify argument boundaries, thereby achieving better performance in TCM event extraction. It has reference value for the inheritance and development of TCM knowledge.