The uncertain factor rainfall is hard to accurately handle and the ant colony clustering algorithm is easy to get caught into suboptimal solution and the searching speed is low in searching space. In order to improve the prediction accuracy of landslide hazard, we propose an uncertain ant colony clustering algorithm based on approximate backbone. Firstly, it utilizes the Gauss point probability model to describe the uncertain data and measure their similarity. Secondly, we introduce the pheromone redistribution and adaptive dynamic variables to update the local pheromone and global pheromone for improving the algorithm's searching speed, and load the genetic algorithm to prevent it from falling into local optimum early. Finally, combining the approximate backbone theory, we build an uncertain ant colony clustering algorithm model based on approximate backbone, which reduces the iteration times and obtains the clustering solution rapidly. Experiments on UCI true datasets and landslide experiment datasets of the Baota district of Yan'an show that the proposed method achieves a higher clustering quality and the prediction accuracy reaches 93.3%, which verifies its feasibility.