Aimed at the problem of low automation and intellectualization of aeroengine wear fault diagnosis,a knowledge acquisition method of aeroengine wear fault diagnosis based on oil data mining was proposed.This method uses selforganizing neural network to fuse the original multidimensional feature data and get the fusion value,uses Parzen window method to establish the limit value of fusion value and divide the sample into normal state,warning state and abnormal state,and uses the software of Weka to extract knowledge rules from oil analysis data.This method can identify different wear state information from oil spectrum data,and extract knowledge rules to build knowledge base of aeroengine wear diagnosis system,which realizes the automation and intellectualization of aeroengine fault diagnosis based on oil spectral wear datalubricant spectrum.The proposed knowledge acquisition method for wear fault diagnosis was validated by using the actual oil spectrum data of an aircraft engine.The results show that the fusion value obtained by data fusion can accurately reflect the deterioration state of aeroengine,it has a high recognition rate by using the boundary value of the fusion value to divide the sample state and extract the knowledge rules.