Aiming at the problem that single parameter characterization performance of aero-engine is not comprehensive, and is easily affected by external environment and flight conditions, a health indicator construction method based on Spectral regression and Gaussian mixture model for multi-source information fusion is proposed. Select the parameters that have complete records and are closely related to the engine health performance, take the difference value of the performance parameters of the left and right engines of the same aircraft as the data source, reduce the dimension of the features through Spectral regression, build a normal state model using the Gaussian mixture model, and then use the distance based on Bayesian inference to characterize the test data and the global distance of the Gaussian mixture model to identify the abnormal state of the engine. Verified by real QAR data from two aero-engine abnormal event cases, the results show that the proposed method can evaluate the health status of aero-engine more effectively and identify engine abnormalities in advance than that of airlines, reserve enough time to make reliable maintenance plans for the engine mechanism, and improve the safety and economy of the aircraft.