In this paper, surface accuracy of the work-piece was improved by mining large amounts of machining data and obtaining potentially valuable information. By using data mining technology, a dynamic milling force prediction model has been established to keep with its working. The model was developed by a combination of Regression Analysis and RBF Neural Network. The internal relation of the data were analyzed in this study, such as milling force, cutting parameters, temperature, vibration and surface quality et.al, and the methods of Cluster Analysis and Correlation Analysis was used to extract and induct dynamic milling force variations on the effects with different situations. The results suggest that the proposed dynamic milling force model had a better prediction effect, which ensure production quality, reduce the occurrence of chatter and provide a more accurate basis for selecting process parameters.