In order to detect and identify unauthorized UAV more accurately，it is necessary to detect and identify the RF signal of UAV under the condition of low SNR.An identification method based on time-frequency spectrogram is proposed to solve the identification problem of UAV RF signal in low SNR.Firstly，the UAV signal is transformed into two-dimensional time-frequency spectrogram by short-time Fourier transform，which is used as the input of neural network.Then，an improved ResNet18 network is built，which introduces channel attention mechanism and spatial attention mechanism，and adopts regularization strategy and adaptive adjustment strategy to improve the identification accuracy of UAV RF signals in low SNR.The experimental results show that，when the SNR is -15 dB，the identification accuracy of proposed model for 16 types of UAV RF signals reaches 0.906 2，which is 0.074 0 higher than that of ResNet18 network，and its performance is better than that of network model methods such as EfficientNet，MobileNetv2 and GoogLeNet. It also shows improved performance under actual noise conditions，demonstrating better robustness to noise and anti-confusion capabilities.