Abstract: Forecasting the trend of coal flow in a short-term is the precondition to realize matching of belt speed and traffic volume. However, the existing short-term prediction methods of coal flow on conveyor belt have the problems of insufficient real-time performance and low precision. For the problems, a short-term prediction method of coal flow on conveyor belt based on support vector machine was proposed. Firstly, the method uses support vector machine algorithm to select the real-time coal flow as dependent variable and statistical data time as independent variable, and then normalizes actual collected coal flow data, uses cross-validation method to select the best parameters. It also uses the best parameters to train the support vector machine to fit ideal short-term prediction curves of coal flow. Finally, the method analyzes fitting degree of the coal flow prediction curves and the original data curves by further comparing the prediction parameters such as mean square error and correlation coefficient to obtain the best prediction curve. The Matlab simulation results show that the method can predict coal flow on conveyor belt in a short time, and the deviation between the predicted data and the true value is small, the mean square error is 0.000 152 563, and the correlation coefficient is 99.784 8%.