Due to the large number of complex service dependencies and componentized modules, a failure in one service often causes one or more related services to fail, making it increasingly difficult to locate the cause of the failure. Therefore, how to effectively detect system faults and locate the root cause of faults quickly and accurately is the focus of current research in the field of microservices. Existing research generally builds a failure relationship model by analyzing the relationship between failures and services and metrics, but there are problems such as insufficient utilization of operation and maintenance data, incomplete modeling of fault information, coarse granularity of root cause localization, etc. Therefore, this study proposes AmazeMap, for which a multi-level fault impact graph modeling method and a microservice fault localization method are designed based on the fault impact graph. Specifically, the multi-level fault impact graph modeling method can comprehensively model the fault information by mining the collected temporal metric data and trace data while system running and considering the interrelationships between different levels; the fault localization method narrows the scope of fault impact, discovers the root cause from service instances and metrics, and finally outputs the most probable root cause of fault and metrics sequence. Based on an open-source benchmark microservice system and the AIOps contest dataset, this study designs experiments to validate AmazeMap, and also compares it with the existing methods. The results confirm AmazeMap’s effectiveness, accuracy, and efficiency.