This study proposes a deep learning model for short-term precipitation forecasting, called MSF-Net, to address the limitations of traditional methods. This model integrates multi-source data, including GPM historical precipitation data, ERA5 meteorological data, radar data, and DEM data. A meteorological feature extraction module is employed to learn the meteorological features of the multi-source data. An attention fusion prediction module is used to achieve feature fusion and short-term precipitation forecasting. The precipitation forecasting results of MSF-Net are compared with those of various artificial intelligence methods. Experimental results indicate that MSF-Net achieves optimal threat score (TS) and bias score (Bias). This suggests that it can enhance the effectiveness of data-driven precipitation forecasting within a 6 h prediction horizon.