In order to solve the problems that the UAV （Unmanned Aerial Vehicle） is vulnerable to environmental interference in image recognition， and the traditional signal recognition is difficult to accurately extract features and has poor real-time performance， a UAV detection and recognition method based on improved CNN （Convolutional Neural Network） and RF （Radio Frequency） fingerprint was proposed. Firstly， a USRP （Universal Software Radio Peripheral） was used for capturing radio signals in an environment， a deviation value was obtained through multi-resolution analysis， to detect whether the radio signal was an unmanned aerial vehicle radio frequency signal or not. Secondly， the detected unmanned aerial vehicle radio frequency signal was subjected to wavelet transformation and PCA （Principal Component Analysis） to obtain a radio frequency signal spectrum which was used as an input of a neural network. Finally， a LRCNN （Lightweight Residual Convolutional Neural Network） was constructed， and the RF spectrum was input to train the network for UAV classification and recognition. Experimental results show that LRCNN can effectively detect and recognize UAV signals， and the average recognition accuracy reaches 84%. When the SNR （Signal-to-Noise Ratio） is greater than 20 dB， the recognition accuracy of LRCNN reaches 88%， which is 31 and 7 percentage points higher than those of SVM （Support Vector Machine） and the original OracleCNN， respectively. Compared with these two methods， LRCNN has improved recognition accuracy and robustness.