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High-resolution weather radar data can reveal the fine structure of detected weather targets and are essential for catastrophic weather analysis, forecasting and warning. Improving weather radar reflectivity data resolution can enhance the monitoring and warning capability of existing operational weather radar for small-and-medium-scale strong convective disastrous weather. In this paper, based on an attention back-projection network (ABPN), the super-resolution reconstruction algorithm is proposed to improve the resolution of weather radar reflectivity data without radar hardware modification. The attentional back-projection network is accomplished by adding long and short skip connections in the deep back-projection network (DBPN) and channel attention mechanism to refine and reconstruct structural features in critical regions. By testing the superresolution reconstruction on real weather processes, it is demonstrated that the ABPN algorithm has significant advantages in radar echo reconstruction quality and subjective visual evaluation, especially in terms of echo details and edge structure features of weather radar.