The development of digital society makes machine learning more and more remarkable in social science research, but machine learning applications in public administration literature is still limited. This article firstly introduces the basic theories of machine learning, and then analyses the applications of machine learning in recently public administration leading journals, and finally summarizes challenges of using this method in public administration research. This article contends that machine learning can help both data collection and data analysis.In data collection, machine learning methods help to turn the non-numeric data into numeric data, as well as data clustering. In data analysis of relativity, machine learning is used to mitigate the dimensional disaster.In causal inference research, this method can analyse the heterogeneous causality, as well as average treatment effect with other causal inference methods such as IV, PSM, RDD, DID and SCM. However, machine learning is challenged in practicability, usability, explanatory and repeatability, which also draws risks from the perspective of public value. Even though, considering its great potential contribution,this article believes that using machine learning in public administration research will be a new trend.