The development of deep learning technology invites most research to consider short-term precipitation nowcasting as a prediction task of radar echo sequences. Due to the nonlinear spatiotemporal transformations involved in the complexity of precipitation, existing short-term nowcasting methods have problems such as low accuracy, short extrapolation time, and difficulty in dealing with complex nonlinear spatiotemporal transformations. To address these issues, this study proposes an S-UNet short-term precipitation forecasting network based on U-Net and LSTM. Firstly, the study introduces the S-UNet layer (SL) module to help the network better extract radar sequence features and construct the overall trend of spatiotemporal changes, thereby improving the network efficiency and increasing the extrapolation duration. Secondly, to better address the complexity of radar echo deformation, accumulation, and dissipation, and to enhance the network’s ability to capture complex spatial relationships and simulate movement trajectories, this study constructs the radar feature (RF) module based on LSTM. Finally, by combining the SL module and the RF module with the U-Net framework, the S-UNet short-term precipitation nowcasting network is proposed, achieving remarkable performance on the KNMI dataset. Experimental results show that, compared with the mainstream methods, on the KNMI’s NL-50 and NL-20 datasets, the proposed method improves the Heidke skill score (HSS) and critical success index (CSI) by 5.25% (6.57%) and 2.17% (4.75%) respectively, reaching 0.30(0.29) and 0.72(0.58); the accuracy increases by 2.10% (1.35%), reaching 0.80 (0.80); and the false acceptance rate decreases by 4.27% (1.80%), dropping to 0.24 (0.38). Additionally, the effectiveness of the proposed modules and their combination methods are verified through ablation experiments.