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To solve the problems of low accuracy of extreme learning machine (ELM) in landslide prediction, and the instability of the model in the training process, RBF Gaussian kernel function is introduced and Xgboost algorithm is used to optimize KELM, and Xgboost KELM prediction model after Xgboost optimization is established; Firstly, the Gaussian kernel RBF is used as the kernel function of the limit learning machine to solve the problem of random mapping of hidden nodes and increase the stability and applicability of the model; Secondly, the cleaned monitoring data is used as the model input, and Xgboost optimization algorithm is used to optimize the super parameters in the kernel function. Xgboost KELM modeling is conducted through four groups of test sets, and the best super parameters are obtained according to the mean square error iteration curve; Finally, two groups of 10% sample sets were used to verify the model evaluation indicators and stability. The experimental results showed that the AUC mean increased by 3 percentage points compared with that before optimization, and the Precision, Accuracy and Recall were at least 1.7 percentage points higher than that of the comparison model. At the same time, the variance and deviation of Xgboost KELM model were small, which proved that the model was stable. The experimental results showed that Xgboost KELM model had a good prediction effect, It has good prediction ability in landslide disaster prediction.