In order to solve the problems that traditional aero-engine anomaly detection methods are difficult to deal with unbalanced samples, low accuracy and generalization performance, and insufficient data distribution consideration, a deep support vector data description based on mixed kernel maximum correntropy criterion (MKMCC-DSVDD) was proposed. Firstly, the synthetic minority oversampling technique (SMOTE) was used to expand the abnormal sample size and improve the generalization performance of non-equilibrium sample. Then, the maximum correlation entropy loss function based on hybrid kernel improvement was established and analyzed to improve the accuracy without data distribution assumption. Finally, an aero engine anomaly detection method based on MKMCC-DSVDD was constructed. The abnormal state average area under curve (AUC) reaches 98.53% in the test of anomaly detection of a certain aero-engine gas path system and oil system, which indicates that MKMCC-DSVDD anomaly detection method has high applicability and generalization performance.