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. In this paper, a dynamic landslide displacement prediction method based on whale-optimized convolutional neural networks (CNN) and bidirectional gated recurrent neural network (BiGRU) is proposed. Firstly, the landslide impact factors are filtered for features and the data set is constructed, the CNN-BiGRU network model is established and the hyperparameter search is performed using the whale optimization algorithm (WOA) , the potential feature vectors are extracted from the landslide data using the CNN network model, and the feature vectors are input to the BiGRU model in the form of time series. BiGRU model, and use its advantage of processing time series data to complete landslide displacement prediction. The results show that the landslide displacement prediction accuracy obtained by the model proposed in this paper is high, and the root mean square error (RMSE) decreases by 0.0305 mm compared with the unoptimized CNN-BiGRU.