With the popularization and promotion of information technology, healthcare big data is growing exponentially, and clinical real-world research based on healthcare big data is receiving increasing attention. The hospital electronic medical record (EMR) records the whole process of diagnosis and treatment of patients in the "real-world", and is one of the most supportive data sources for clinical decision-making. However, the existence of a large number of unstructured text data in EMR data increases the difficulty of data processing and restricts the development of research based on EMR data. Advanced methods such as information technology and artificial intelligence need to be applied to the processing of unstructured EMR data to accelerate the transformation of data value. This paper summarizes the current common methods of unstructured medical data processing, including methods based on dictionaries and rules, methods based on traditional machine learning and deep learning, and methods based on cognitive models represented by ontology, and also discusses the problems of standardization and transparent reporting when processing unstructured EMR data and looks forward to the relevant development.

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