Hailstorms are characterized by their suddenness, localized nature and high destructive power. Although observations acquired by ground-based automatic stations, radars and satellites play a certain role in hail identification, the limitation of single observation data leads to a high false alarm rate and low accuracy rate in hail identification. Therefore, there is an urgent need to construct a hail identification technology based on multi-source high-resolution observation. In this paper, a multi-source data fusion network for hail recognition is proposed. The deep learning method utilizes the spatio-temporal feature extraction module, the multi-source data feature fusion module, and the UCUNet (U Connection Unet) recognition module to fully exploit the spatio-temporal features of the multi-source data such as FY4B (FengYun-4B star) satellites, weather radar, and numerical models when hail occurs, and innovatively adds the topographic height, slope, and slope direction as hail recognition factors. In order to evaluate the performance of the proposed network method, this paper conducts a series of experiments and compares the experimental results with real labeled data. The results show that HINet (Hail Identification Net) can make full use of multi-source data and effectively improve the hail identification results under complex terrain conditions. The network model proposed in this paper has high accuracy and practicality in hail research and identification.