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In order to solve the problem of poor accuracy and local optimal caused by multiple and complex influencing factors of debris flow in mountainous villages and towns and the random parameters of LSSVM algorithm, the LSSVM debris flow disaster prediction model was established by KPCA dimension reduction and SSA algorithm parameter optimization methods. Mudslides son duong district of villages and towns, for example, global topography by factor analysis of debris flow, wash specification for data preprocessing, 6 by using KPCA principal component contribution rate to select the factors as the input data of LSSVM algorithm, debris flow occurrence probability as output, debris flow forecast model is established, and model parameters are optimized with the SSA algorithm. By comparing the prediction results of LSSVM optimized by SSA with those of GA and GC parameter optimization models, the results show that the accuracy of SSA-LSSVM reaches 93.2%, which is higher than that of other models [4.8%-1.4%]. Moreover, MAE, MSE and RMSE of LSSVM optimized by SSA algorithm are minimum and close to zero. At the same time, the results are compared and analyzed from the prediction grade dimension of debris flow occurrence, and the results further illustrate the accuracy and robustness of the model prediction. This study shows that SSA-LSSVM algorithm can be used to predict the probability of debris flow disasters, and provides a scientific basis for the prediction of such disasters.