Aiming at the problem of accurate recognition of tool wear state in titanium alloy processing, a wear prediction model 〖JP2〗for titanium alloy tool based on support vector machine (SVM) and whale optimization algorithm (WOA) was established. A new WOA-〖JP〗SVM model was proposed by combining SVM and WOA for accurate estimation of titanium alloy end milling tool wear. By extracting the signal feature of cutting force as monitoring feature, the dimension reduction of monitoring feature was realized by using neighborhood preserving embedding (NPE),by which the modeling efficiency of WOA-SVM model was improved. The experimental results show that using NPE reduces the modeling time of WOA-SVM by more than 90% on the premise of ensuring the prediction accuracy. Compared with the common methods such as PSO-SVM and GSA-SVM, WOA-SVM has higher prediction accuracy and the modeling time is decreased by more than 30%. The model can be used to effectively predict the wear state of titanium alloy cutting tools.