For the static machine learning model is difficult to reflect the dynamic evolution characteristics of landslide effectively, and there is the problem that the displacement prediction results are unstable due to the forgetfulness of historical data when the time series is too long. A dynamic landslide displacement prediction method based on whale-optimized convolutional neural networks (CNN) and bidirectional gated recurrent neural network (BiGRU) was proposed. Firstly, the landslide impact factors were filtered for features and the data set was constructed, the CNN-BiGRU network model was established and the hyperparameter search was performed using the whale optimization algorithm (WOA), the potential feature vectors were extracted from the landslide data using the CNN network model, and the feature vectors were input to the BiGRU model in the form of time series, and used its advantage of processing time series data to complete landslide displacement prediction. The results show that the landslide displacement prediction accuracy obtained by the proposed model is high, and the root mean square error (RMSE) decreases by 0.030 5 mm compared with the unoptimized CNN-BiGRU.