Transfer learning based intelligent fault diagnosis method has become an important research direction in the field of mechanical equipment fault diagnosis. However，most of the existing fault diagnosis models cannot reasonably calculate the importance of marginal and conditional distributions in the process of transfer learning，and different data distribution will lead to different diagnostic results. To solve such problem，a deep convolution dynamic adversarial transfer network is proposed for intelligent fault diagnosis of spindle bearing. One-dimension convolutional neural network is used to extract transferable features. A dynamic adversarial learning strategy is introduced into the proposed method. The importance of marginal and conditional distributions in transfer learning is calculated according to the similarity of data distributions，which effectively improves the diagnostic accuracy. The effectiveness of the proposed method is verified in spindle bearing fault diagnosis of industrial machine tools. The experimental results show that the proposed method can powerfully explore fault features and realize knowledge transfer between different working conditions，which has important significance for the practical application industry.