The process of landslides for mine dumps is a dynamic, large-delay, and complex situational problem. There are many factors that affect the landslide in mine dumps, and each characteristic index influences each other. However, there is no strict categorizing standard for index of landslide warning for dumping sites. This study proposes Principal Component Analysis Long-Term and Short-Term Memory network (PCA-LSTM) model, using PCA and correlation analysis, mining the first principal component among all the characteristic indicators, and the other indicators with strong correlation with the first principal component. The obtained other characteristic indexes and the first principal component are used as the main characteristic indicators to predict the dumping landslide, and the LSTM is used to combine the existing input information and the historical information when dealing with time series problems. The LSTM model predicts the displacement of the first principal component through a number of other characteristic indicators and has obtained sound results.