High spatiotemporal resolution rainfall estimation is closely related to transportation, tourism, agricultural irrigation and people's daily travel. However, accurate high-resolution rain/no-rain classification is a very challenging problem. This paper proposes a multi-source data based multi-view learning method for rain/no-rain classification. The multiple source data used in this paper include radar, satellite and ground observation factors and rain/no-rain observation data. This method can be summarized as follows. Firstly, VisCAPPI view and VisPPI views are constructed according to the radar observation factors. VisSat view is constructed from the sunflower satellite data. VisGround view is constructed according to the ground observation factors. Secondly, the views of VisCAPPI_PPI, VisRadar_Sat, VisRadar_Groumd, VisSat_Ground, and VisRadar_Sat_Ground are obtained by combining features from preconstructed views. Random forest (RF) classification models are trained from these views using RF method. Finally, the rain/no rain classification results are obtained from the estimated results of these RF classification models. The main contributions of this paper arelisted as follows:(1) Present a method for constructing VisCAPPI, VisPPI, VisSat and VisGround views and their feature combined views for radar, satellite and ground observations; (2) A multi-view weight random forest method (MVWRF) is proposed. Multi-source data of radar, satellite and near surface observations are fused for rain/no-rain classification with temporal resolution of 6-minute and spatial resolution of 1km×1km in virtue of the proposed method. The experimental results show that the proposed method in this paper can obtain high precision of rain/no-rain classification after training and testing on 393 meteorological stations covered by radar in Quanzhou on October 7 and 8, 2016.