The invention discloses an airborne network intrusion detection method based on deep learning. The method includes the steps of data packet monitoring, data packet collection, time stamp labeling, filtering sampling, feature mapping, character-type feature digitization, normalization processing, feature learning, performance evaluation and the like. Compared with traditional detection models, an airborne network intrusion detection model generated by the method provided by the invention has better detection performance, and decreases probability that an airborne network is attacked due to missed detection or false reporting.