Awareness of existing unmanned aerial vehicle identification method being visual detection, and easily affected by weather changes and many other factors such as visible detection range, and the surrounding buildings shade, etc., a convolution of the neural network based on depth unmanned aerial vehicle link perceptual recognition algorithm is proposed, giving a multimode multitype uavs RF signal database build steps, and the proposed convolution neural network is designed and optimization method is made in detail. The measured results show that the depth algorithm proposed in this paper can not only realize multibatch and multitarget UAV intrusion identification, but also further distinguish its model from flight mode. Under condition of low signaltonoise ratio as low as -20 dB, the uav batch identification rate is 96.8% (6 categories), and the flight mode identification rate is 94.4% (12 categories). This method is prosperous in a strong application.