Emergency neurology covers a wide range of conditions, often involving urgent situations such as acute cerebrovascular diseases, seizures, central nervous system infections, and consciousness disorders. However, due to the time constraints in emergency care and limited patient information collection, misdiagnosis and missed diagnoses are common issues. Large language models (LLMs) possess powerful natural language processing and knowledge reasoning capabilities, enabling them to directly handle and understand complex, unstructured medical data such as patient medical records, dialogue notes, and laboratory test results. LLMs show broad potential for application in complex medical scenarios. This study aims to evaluate the application value of LLMs in emergency neurology, specifically examining their diagnostic accuracy in emergency neurology conditions, analyzing the feasibility of treatment plans and further examination recommendations proposed by the model, and exploring their potential in improving diagnostic efficiency and aiding decision-making.