|Abstract:Aiming at the shortcomings of traditional slope stability prediction model, a support vector machine model (GS-PSO-SVM) based on grid search and particle swarm optimization is proposed. In order to solve the problem of parameter selection of support vector machine, the grid search method is used to roughly optimize the parameter range, and then the particle swarm optimization is used. Using this model to predict the slope example, 30 of the 39 sample samples are training samples, and the remaining 9 are used as prediction samples, with rock gravity, cohesion, internal friction angle, slope angle, slope height, and porosity. The influence factors of the six slope stability of water pressure are taken as input, the slope stability state is taken as the output, and the prediction result is compared with the separate grid search method, particle swarm optimization algorithm and genetic algorithm optimization support vector machine model. The results show that the classification accuracy of GS-PSO-SVM model is 100%, and it has better prediction accuracy and higher prediction efficiency. The model can effectively predict the slope stability state.