Landslides and other disaster problems caused by highway slope instability have a significant impact on people's lives, so the establishment of landslide prediction models is of great significance to the prevention and control of geological disasters. Long short term memory(LSTM) model based on Bayesian optimization algorithm (BOA) WAs proposed to investigate the effect of soil pressure changes on slope stability. The highway slope in Nanping City, Fujian Province was taken as an example. The correlation of multiple landslide influencing factors were integrated in the model, especially the time lag between each influencing factor, and the lag time was calculated. The four hyperparameters of the LSTM model (time step, number of hidden elements, number of iterations, and batch size) were automatically searched and optimized, which solves the problem of needing to manually tune the parameters when building landslide prediction models with the LSTM model. To verify the accuracy and effectiveness of the model, the model was compared with recurrent neural network(RNN) and LSTM models, and the monitoring data set of soil pressure on highway embankments in Nanping City was used as a control. The results show that compared with the other two models, the mean square error(MSE), mean absolute error(MAE) and mean absolute percentage error(MAPE) of the BOA-LSTM model are reduced by 52.4% and 59.9%, 28.8% and 30.1%, and 30.2% and 29.9% respectively, and the prediction accuracy is improved from about 95% to 96.56%, and the coefficient of determination is also closer to 1, indicating that the model can more accurately predict the earth pressure change and provide effective data support for slope deformation and stability analysis.