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Traditional health insurance information fraud detection algorithm has many problems such as long running time and low efficiency, which can not guarantee the safety of medical insurance information. In order to solve this problem, we use random forest algorithm to detect medical fraud information in unstable network. Through extracting mixed sampling instability in the case of data, and the establishment of the imbalanced data classification algorithm for iterative sampling mechanism; random forest data, builds a classifier using voting method, and on the basis of screening of abnormal data; the algorithm of insurance fraud detection using information model. Design comparison experiment to verify the effectiveness of the algorithm. The experimental results show that the run time based on random forest algorithm is shorter and more efficient.