The invention discloses a mobile ad hoc network intrusion detection method and device based on deep learning, relating to the field of wireless network safety. The device comprises a data acquisition module, a data fusion module, a preprocessing module, a storage module, an intrusion detection module and a response warning module. After fusion and redundancy elimination of captured wireless data packages, network behavior features are extracted and stored; after deep learning of the network behavior features, a deep neural network model expressing network behaviors is established; and to-be-detected network data is input into the deep neural network model, after intrusion is judged and recognized, response and warning are performed. According to the method, network behavior feature vectors which are detected and are considered to be abnormal are stored and are used for training the deep neutral network. When occurring again, the intrusion types can be detected and recognized. While the model training and detection efficiency are guaranteed, the detection accuracy is improved, and the safety of the mobile ad hoc network is further improved.