The invention discloses an unmanned aerial vehicle load engine state identification method based on
deep learning, and the method comprises the following steps: S1, collecting the multi-mode operation data of an unmanned aerial vehicle load engine, and completing the
standardization preprocessing; s2, inputting the spatial data into a spatial
pyramid pooling network, and extracting a multi-scale spatial
feature matrix; s3, inputting the spatial features into a masking auto-
encoder, performing feature masking, encoding and reconstruction, and generating reconstruction features and error information; s4, fusing the original features, the reconstructed features and the non-spatial data, inputting a discrimination network, and outputting a health
label and a risk
score; and S5, according to an identification result, a flight
control system or a maintenance platform is linked to realize alarm and scheduling. According to the method, intelligent identification and abnormal risk early warning of the state of the load engine of the unmanned aerial vehicle are realized by fusing multi-scale spatial
feature extraction and a covering self-encoding reconstruction mechanism.