The increasing complexity of software application brings great challenges to software security. Due to the increase of software scale and diversity of vulnerability forms, the high false positives and false negatives of traditional vulnerability mining methods cannot meet the requirements of software security analysis. In recent years, with the rise of artificial intelligence industry, a large number of machine learning methods have been tried to solve the problem of software vulnerability mining. Firstly, the latest research results of applying machine learning method to the research of vulnerability mining are summarized in recent years, and the technical characteristics and workflow are proposed. Then, starting from the core original data features extraction, the existing research is classified according to the code representation form, and the existing research is systematically compared. Finally, based on the summary of the existing research, the challenges in the field of software vulnerability mining based on machine learning are discussed, and the development trends of this field are proposed.