In recent years, UAV technology has been widely used in many fields. Radar detection is widely used because of its advantages of long-distance, high-precision positioning and rapid response, and the research on micro-Doppler characteristics of UAV has attracted much attention. However, the echo signal of UAV is susceptible to interference in complex environment, resulting in time-frequency characteristics distortion. The traditional time-frequency analysis methods have limitations in dealing with such problems. Therefore, this paper proposes a time-frequency curve reconstruction algorithm for UAV based on deep learning. By designing autoencoder model based on convolutional neural network SelfNet, effective information is extracted from noise interference and channel distortion, and high-quality time-frequency curve is reconstructed. SelfNet uses the encoder to extract the characteristics of time-frequency curve, and restores the signal structure through the decoder. The experimental results show that the average PSNR is 17.767 2, and the average SSIM is 0.431 7, which is better than classical convolutional neural networks such as GoogLeNet and ResNet, and its generalization ability is verified by small sample experiments and transfer learning, which provides an idea for UAV time-frequency curve reconstruction in complex environment.