China’s topography is characterized by significant, and in practical engineering, it often suffers from the impact of rainfall, leading to geological disasters such as mudslides and landslides. Numerous facts have shown that slope failures, including landslides, mostly occur during or after rainfall, indicating that rainfall is one of the main causes of slope instability. However, due to the different soil types in various regions of our country, the reasons for slope instability caused by the differences in various properties of soil are also different. Therefore, the application of slope reliability theory in slope stability analysis is a hot topic in the field of geotechnical engineering. How to accurately analyze the reliability index and failure probability of slopes under different geological conditions and soil parameters has important engineering and practical significance. There are many studies on the impact of rainfall on slope reliability and the spatial variability of soil both domestically and internationally. Based on the lack of research on the simultaneous consideration of rainfall impact and spatial variability on slopes, this paper systematically summarizes the various impacts of rainfall on soil spatial variability and also considers the use of deep learning-based computer deep networks under the background of artificial intelligence development to establish relevant models for slope monitoring and early warning.