This study applies the C4.5 algorithm to classify tropical cyclone (TC) intensity change in the western North Pacific. The 24 h change in TC intensity (i.e., intensifying and weakening) is regarded as a binary classification problem. A decision tree, with three variables and five leaf nodes, is built by the C4.5 algorithm. The variables include intensification potential (maximum potential intensity minus current intensity), previous 12 h intensity change, and zonal wind shear. All five rules, discovered from the tree by forming a path from the root node to each leaf node, can be interpreted by theories on TC intensification. Data mining results identify a predictor set (i.e., the mined rules) with high classification accuracy. The present study suggests that this data mining approach can shed some light on investigating TC intensity change processes and therefore has the potential to improve the forecasting of TC intensity.