Since the rainfall and other uncertainties are difficult to effectively deal with in landside hazard prediction, as well as the density threshold in CFSFDP algorithm is required to be set manually and its low accuracy for large-scale data clustering, in order to improve the prediction accuracy, this study proposed an uncertain CFSFDP algorithm based on Grid and Merging clusters (uncertain GM-CFSFDP). Firstly, the E-ML distance formula based on uncertain data processing method is designed to effectively describe the uncertain factors of rainfall. Secondly, the idea of meshing is used to effectively encode the large-scale data by dividing it into multiple grid spaces. The average density of the mesh is calculated to establish the grid density threshold distribution model and obtain the grid density threshold dynamically. Finally, the hierarchical clustering idea is used to merge the higher association class and the uncertain GM-CFSFDP algorithm model is established. The experiments conducted in the Baota district of Yan'an show that the uncertain GM-CFSFDP clustering algorithm achieves a higher prediction accuracy and proves the feasibility and advancement of the algorithm in landslide hazard prediction.