The landslide, the evolution of which usually occurs under complex geological conditions, and which brings about great damage to human life and property, is a common geological disaster. Understanding the development of landslides is important for the prevention and control of these disasters. Using field time series data on cumulative landslide displacement, a landslide displacement prediction method based on the Genetic Simulated Annealing algorithm was proposed. The Genetic Simulated Annealing algorithm optimized BP neural network was used to analyze observation point Z118 in the Baishui River landslide warning area. The cumulative displacement data of the first 3 months was applied to predict the accumulated displacement of the 4 month. The results of the BP neural network model and the Elman neural network model were compared. At the same time, the prediction results of the Genetic Simulated Annealing algorithm and the Support Vector Machine model were compared. The results showed that the landslide displacement prediction model established in this article can improve the accuracy of the prediction, and provide a reference for landslide displacement prediction in engineering construction.