Abstract: Dynamic lateral pressure during silo unloading is an important cause of silo structure damage. However, there are many factors affecting the dynamic lateral pressure of silo, and there is a negative complex nonlinear relationship among them. Therefore, it is particularly important to establish an efficient and accurate prediction method for dynamic lateral pressure considering multiple factors. Based on the machine learning method, three machine learning methods of support vector machine, BP neural network and random forest were applied to predict the dynamic lateral pressure of silo. Firstly, the relevant factors affecting the dynamic side pressure of silo were selected as input variables, and the dynamic side pressure was the output value. Then, the parameters of three commonly used machine learning were optimized and set, and the dynamic lateral pressure prediction model of silo was established. The prediction model was tested by test samples, and the analysis showed that the support vector machine algorithm had the optimal prediction ability and applicability, which provided a new method for the prediction of dynamic lateral pressure of silo. Finally, MATLAB software was used to conduct random sampling on the single influencing factor of storage density, and 1 000 groups of uniformly distributed random numbers were obtained. The data was input into the optimal prediction model, and the probability distribution of the predicted value was fitted by Easyfit software. The probability distribution of the dynamic lateral pressure of the silo was obtained, which provided a theoretical basis for the reliability study of the silo structure.