As the number of spacecrafts increasing, it is particularly important to diagnose the fault of spacecraft tracking telemetry and control (TT&C) system quickly and accurately. To address the problems of large changes in the space environment, complex telemetry data components and low accuracy of fault diagnosis, a fault diagnosis method of spacecraft TT&C system based on the attention mechanism residual network (AM-ResNet) is proposed. Firstly, the telemetry data are converted into grayscale image. Secondly, the image is passed through the residual network (ResNet) and attention module to obtain feature map with global dependence. Finally, the softmax classifier is used to achieve image classification after convolution and pooling operations to realize the fault diagnosis of spacecraft TT&C system. Experimental results show that the fault diagnosis method of spacecraft TT&C system based on the proposed AM-ResNet can improve the accuracy of fault diagnosis to be 95. 68% . Compared with ResNet-18, AlexNet and LeNet-5 fault diagnosis models, the diagnostic accuracy is increased by 3. 53% , 5. 62% and 16. 43% , respectively, which prove that the method can effectively improve the fault diagnosis performance of the spacecraft TT & C system.