This paper describes a support vector machine(SVM) approach to improve the test validity and accuracy for Aero-engine fault diagnosis. A new concept called classification rate has been introduced. The paper presents a new information fusion fault diagnosis method based on SVM. The diagnostic decision rules have been improved and applied to aero-engine gas path fault diagnosis. The test result manifests SVM has distinguish limitation to a strong linear correlation of two types of fault samples, it may also have diagnostic difficulties caused by Maximum number of classifications. Therefore, this paper has then proposed a corresponding solution. The simulation results verify that the method is feasible and it can reduce the defect caused by small sample data. It has high capacity of resisting disturbance and high accuracy.