In the Three Gorges major landslides are the primary disasters, and endanger the normal running of the Three Gorges Dam and the life and property of the residents in the region. Hence, it is very important to formulate effective strategies for the prevention and remediation of landslides in this region, as part of which landslide mechanism analysis is an important task. In this paper, landslide mechanism analysis in the Three Gorges is carried out based on spatial data mining and knowledge discovery. The 1:50000 geological map, 1:10000 relief map and China–Brazil Earth Resources Satellite (Cbers) images were adopted to produce the key factors influencing landslide development, including engineering rock group, reservoir water fluctuation, vegetation coverage, slope structure, elevation, slope and aspect. A soft partition method was adopted to elevate the knowledge levels and formulate the quantification factors qualitatively based on the cloud model. In terms of these qualitative factors, a concept grid is built based on formal concept analysis and a concept grid algorithm. Based on this concept grid, the knowledge related to landslide mechanism is mined from the multi-theme landslide data, including the associations between the various factors that influence a landslide, the circumstances in which a landslide is easily triggered, and the relationship between landslide probability and factor combination. The experimental results show that the knowledge of landslide causes mined by our method possesses high confidence and is in agreement with the field circumstances. Therefore, the spatial data mining method proposed in this paper is suitable for landslide mechanism analysis. It can achieve the transformation between quantitative detection data of landslides and qualitative human mind, thereby leading to an innovative approach for landslide mechanism analysis.