Aimed at the wear process of gear wear, vibration signals were able used to characterize wear intensity, wear prediction was able achieved by the prediction of vibration signals, a wear prediction algorithm based on wavelet kernel support vector machine (SVM) was proposed. Firstly, the least square wavelet modeling method in the wear prediction was analyzed, and wavelet kernel was used as the kernel function to improve the nonlinear performance of the system. Then the SVM parameters were optimized by the Quantumbehaved Particle Swarm Optimization (QPSO), and the system possessed faster searching speed and maintained the characteristics of time series. The statistical indicators of the vibration of gear box were used in validation experiment to characterize gear wear intensity. The results of the experiment show that the prediction method can effectively predict gear wear. 