A fault root cause identification method, wherein the method comprises: determining, from a plurality of first success flows based on a first failure flow in a first network, a first target success flow related to the first failure flow, wherein there is a similarity between the first target success flow and the first failure flow met a first preset condition; and determining a target fault root cause of the first failure flow based on one or more feature indicators of the first failure flow, feature indicators of the first target success flow, and a trained first machine learning model; wherein that there is a similarity between the first target success flow and the first failure flow met a first preset condition is specifically: the similarity between the first target success flow and the first failure flow is greater than a preset similarity threshold; or the similarity between the first target success flow and the first failure flow belongs to first N maximum similarities between the plurality of first success flows and the first failure flow in the first network, wherein N is a preset value; wherein the determining a target fault root cause of the first failure flow based on one or more feature indicators of the first failure flow, feature indicators of the first target success flow, and a trained first machine learning model comprises: determining a difference between the feature indicators of the first failure flow and the feature indicators of the target success flow; and determining the target fault root cause of the first failure flow based on the respective feature indicators of the first failure flow and the target success flow that are different from each other. determining, from a plurality of first success flows based on a first failure flow in a first network, a first target success flow related to the first failure flow, wherein there is a similarity between the first target success flow and the first failure flow met a first preset condition; and determining a target fault root cause of the first failure flow based on one or more feature indicators of the first failure flow, feature indicators of the first target success flow, and a trained first machine learning model; wherein that there is a similarity between the first target success flow and the first failure flow met a first preset condition is specifically: the similarity between the first target success flow and the first failure flow is greater than a preset similarity threshold; or the similarity between the first target success flow and the first failure flow belongs to first N maximum similarities between the plurality of first success flows and the first failure flow in the first network, wherein N is a preset value; wherein the determining a target fault root cause of the first failure flow based on one or more feature indicators of the first failure flow, feature indicators of the first target success flow, and a trained first machine learning model comprises: determining a difference between the feature indicators of the first failure flow and the feature indicators of the target success flow; and determining the target fault root cause of the first failure flow based on the respective feature indicators of the first failure flow and the target success flow that are different from each other. the similarity between the first target success flow and the first failure flow is greater than a preset similarity threshold; or the similarity between the first target success flow and the first failure flow belongs to first N maximum similarities between the plurality of first success flows and the first failure flow in the first network, wherein N is a preset value; wherein the determining a target fault root cause of the first failure flow based on one or more feature indicators of the first failure flow, feature indicators of the first target success flow, and a trained first machine learning model comprises: determining a difference between the feature indicators of the first failure flow and the feature indicators of the target success flow; and determining the target fault root cause of the first failure flow based on the respective feature indicators of the first failure flow and the target success flow that are different from each other.