Abstract: Surface displacement prediction is of great significance in landslide monitoring and early warning, and establishing a stable and reliable landslide displacement prediction model is crucial. This paper utilizes a convolutional neural network (CNN) and attention mechanism to predict landslide displacement, and takes the Huangniba Dengkan landslide in the Three Gorges reservoir area as an example for verification. This paper comprehensively analyzes the landslide's monitoring data on rainfall, reservoir water level, and surface displacement for 8 years. It establishes a CNN-BiLSTM-Attention deep learning combination prediction model, and uses adaptive learning rate and regularization techniques for model training, improving the generalization ability of the model while avoiding overfitting. Additionally, the model is subjected to comparative validation with the traditional long short-term memory (LSTM) model. The results show that the model's landslide displacement prediction accuracy has been significantly enhanced compared to traditional machine learning and neural network methods. The prediction model's goodness of fit (R²) reaches 0.989, and the mean absolute percentage error (MAPE) is merely 0.059.