Abstract:The safety factor is one of the important indicators used to evaluate slope stability. The complex slope system leads to uncertainty in the safety factor prediction. Therefore, in order to obtain a more reliable safety factor and at the same time solve the shortcoming that the Pelican Algorithm (POA) easily falls into a local optimum as the number of iterations increases, this paper proposes a multi-strategy Pelican Algorithm (IPOA) and sup-port vectors. The regression model combined with machine (SVR) is used to predict the slope safety factor. First, the original Pelican algorithm was improved by integrating multiple strategies; then the improved Pelican algorithm was combined with the support vector machine to select six influencing factors as input layer indica-tors of the IPOA-SVR model and train the model to obtain IPOA-SVR. SVR slope stability prediction model; finally, compare with KNN, RF and Adaboost models, and calculate the mean square error (MSE) of each model on the training set and test set to verify the superiority of the IPOA-SVR model . The experimental re-sults show that compared with other models, the IPOA-SVR model has better optimization performance. The mean square error on the test set is 0.0309 and the correlation coefficient is 0.91. This illustrates the effective-ness of the strategy used in this article for the POA algorithm. The IPOA-SVR model can Provide a solid tech-nical foundation for the prediction of slope instability disasters.