To address the performance degradation problem of aero piston engine caused by different blockage degrees of intake and exhaust, a two-channel deep perspective feature fusion diagnostic model based on conventional intake or exhaust and cylinder combustion data was designed. So the self-attention (SA) mechanism was introduced into the combustion perspective channel of the constructed two-channel deep convolutional neural network (DCNN) diagnostic architecture, which enhanced the ability to extract combustion features. By setting five health levels of different degrees for intake or exhaust blockage, a performance degradation dataset was obtained for the ground bench tests at the altitude of 1 920 m and engine AMESim+Simulink joint simulations, including two typical operating conditions: takeoff and cruise. Using the takeoff condition at a propeller speed of 2 300 r/min as a study case, the trend analysis of cylinder pressure changed with different blockage degrees of intake or exhaust, the t-SNE depth feature distribution and classification diagnosis analysis of each network layer were carried out. And the rationality of the diagnostic architecture was further verified by the model component ablation experiment. The results showed that the two-channel diagnostic model of self-attention and deep convolutional neural network (SA-DCNN) for cases of intake or exhaust blockage on aero piston engine achieved an average accuracy of 98.95% and 98.62% on five levels of health diagnosis, respectively indicating that the diagnostic model had high accuracy.