Addressing the limitations of traditional methods for predicting slope stability, the low accuracy and performance of single models, and the drawbacks of conventional hyperparameter optimization methods, a slope stability prediction database was established based on the collected statistical data of slope height, slope angle, unit weight, cohesion, internal friction angle, and pore pressure ratio from 276 slope stability samples at home and abroad. A slope stability prediction model based on Bayesian optimization (BO), convolutional neural networks (CNN), and long short-term memory artificial neural networks (LSTM) was constructed. The model hyperparameters of this composite model were optimized through BO to prevent overfitting and enhance model performance. By combining the strengths of both CNN and LSTM, the training efficiency and generalization capability of the combined model was improved and the risk of overfitting was reduced. The performance of the model presented was evaluated using five evaluation indicators and conducting a comparative analysis with five different prediction models. The results show that the accuracy, precision, recall, F₁ score, and area under curve(AUC) of the model are 92.77%, 95.74%, 91.84%, 0.937 5, and 0.930, the model presented exhibits superior performance compared to the other comparison models. The BO-CNN-LSTM model was applied to a total of nine slope engineering cases in Lala copper mine and Dongmiaojia landslide for validation, and the prediction results were consistent with the actual engineering, proving the model to be scientifically reliable.