[Abstract] Monitoring and forecasting of landslides is an effective means and feasible method to reduce landslide disasters. The traditional monitoring methods have poor timeliness, and the prediction models established by using statistical forecasting models and deterministic models are relatively inaccurate. In order to improve this problem, a landslide early warning research method based on comprehensive measurement and combination of extreme learning machine and entropy method is proposed. Analyze the impact factors of landslide, a landslide simulation monitoring platform was build, and monitor the comprehensive parameters affecting landslide by rainfall, soil shallow water content, soil deep water content, sliding stress, underground displacement, etc. Entropy method is applied to the evaluation of landslide, and its comprehensive score is taken as the risk parameter and the comprehensive measurement parameter as the training samples to build an extreme learning machine (Extreme Learning Machine). The results show that under the comprehensive measurement method, the results of the early warning model which combines the entropy method with the extreme learning machine algorithm are consistent with the actual situation, and the predicted values are same as the measured values; the lowest accuracy of the model is 98.48%, higher than that of the BP neural network; and the learning speed of the network is obviously improved. It is concluded that this method is feasible for landslide prediction, which is suitable for complex and non-linear landslide prediction, and provides a feasible method for landslide early warning model.