Mine hoist is the throat of mine production, and its reliability plays an important role in safe and efficient production of coal mine. Hoist fault prediction is an important measure to improve the reliability of hoist. Aiming at the problem of large relative error of traditional hoist fault prediction model, this paper establishes a hoist fault prediction model based on least squares support vector machine. After training the model with the collected data of the hoist, the real-time data of the hoist are predicted to find out whether it is possible to be abnormal. The experimental results show that the average absolute percentage error and mean square error of the model are 1.1301% and 1.1663 respectively, which are lower than the wavelet neural network prediction model, and it has shorter running time and faster convergence speed. The prediction method can accurately find the fault of mine hoist in advance and provide an important basis for the predictive maintenance of mine hoist.