Aiming at the problem that early fault characteristic signal of bearing in gear box is weak and affected by the environmental noise seriously,which makes the fault feature information is difficult to identify,a early fault diagnosis method of gear box is proposed based on dual-tree complex wavelet transform and minimum entropy deconvolution. Firstly,the dual-tree complex wavelet transform method was used to decompose the collected vibration signals into several components with different frequency bands.. However,due to the interference of noise,it was difficult to make a correct judgment from the spectrum of the components. Then,the component that contains fault feature was processed by using minimum entropy deconvolution to reduce the strong background noise and enhance the fault information. Finally,the Hilbert envelope spectrum analysis was performed to identify the frequency of the bearing fault. The effectiveness and superiority of the proposed method are verified by Simulation experiment of bearing fault in gear box and engineering application.