Slope failure is a major challenge in engineering construction and geological hazard prevention in mountainous regions of China. When the spatial variability of geotechnical parameters is considered, a fixed geometric search pattern was used in the traditional limit equilibrium method. Potential slip surfaces cannot be accurately captured, and the computational efficiency is low. These shortcomings cannot meet the demand for rapid and accurate stability assessment in large-scale slope projects. An intelligent analysis method was proposed by integrating deep learning with rigorous limit equilibrium theory. Convolutional neural networks (CNN) and long short-term memory networks (LSTM) were coupled, and an attention mechanism was introduced. This method was used to learn the spatial distribution characteristics of randomly varying strength parameters and to predict slip surfaces and safety factors. The results show that the CNN-LSTM-Attention model achieves a precision of 0.996 8 in predicting slip surfaces and an R² of 0.972 5 in predicting safety factors. The predictions of the deep learning model are highly consistent with those obtained by the limit equilibrium method. It is concluded that the proposed model provides intelligent support for slope stability evaluation in engineering practice.