In view of the complex structure and severe working conditions of gas turbine, a multi-channel convolutional neural network (MCCNN) deep transfer learning gas turbine rotor fault diagnosis was proposed for the problem that it was difficult to obtain the rotor system fault samples during operation and the fault diagnosis accuracy was low due to the small sample size. The method firstly, took the one-dimensional raw vibration signal of the bearing as the input, then rearranged and combined the data to simulate the converted twodimensional image, effectively avoiding the tedious operation of the actual converted image. The MCCNN model was trained with the public bearing data from Case Western Reserve University (CWRU) and Xi′an Jiaotong University (XJTU) to update the weights and diagnose. The fault classification accuracy is up to 99. 95% ~ 100%. CWRU bearing fault datasets were used as the source domain and the gas turbine rotor fault datasets were used as the target domain, the model parameters obtained from the source domain training were retrained by using transfer learning method for the target domain datasets and the classification accuracy for the gas turbine fault data was 97. 78%. The experimental results demonstrated that multi-channel convolutional neural networks and transfer learning model is suitable to the task needs and can solve the problem with a small sample size of rotor system.