【Objective】To research the construction and optimization of natural language processing model for unstructured medical records，and using the model to extract structured data from medical records of stroke patients in Jiangxi Medical Big Data Platform.【Methods】According to the actual needs of clinical research，a stroke specialist entity annotation system and named entity annotation corpus were constructed based on 500 hospital admission records of stroke patients，which randomly selected between 2011 to 2016 from the Jiangxi provincial medical big data platform. The corpus is used to construct a named entity extraction model based on CRF and RUTA rules，and the recognition accuracy is improved by adjusting RUTA rules and parameters.【Results】Accuracy rate of extraction model was 0.960，recall rate was 0.916 and F-score was 0.939. The extraction model was used to extract 264 580 entities and 1 161 077 entity relationfrom 10 295 stroke patients′ admission records of the medical big data platform.【Conclusions】The constructed natural language extraction model has a high recognition accuracy，which can accurately obtain valuable scientific research data of patients′ past history，life history and clinical manifestations from a large number of unstructured medical records and effectively improve the clinical research efficiency and scientific research level of cerebrovascular diseases.