Abstract: By using the feature extraction strategy of local extraction and global integration, this paper proposes a multi-block convolutional variational information bottleneck (MBCVIB) for the fault diagnosis of multivariate dynamic processes. Firstly, according to the process mechanism, all variables are divided into sub-blocks and the variables in the same operation unit will be put into the same block. Secondly, one-dimension convolutional neural network (1-D CNN) is used to extract the local dynamic features of each operating unit in the process, which considers the temporal correlation between samples. Besides, a global feature representation is constructed by concatenating the local dynamic features of all operating units. On the basis of global features, the most relevant fault information is further extracted according to the variational information bottleneck principle. Finally, the proposed model is validated via Continuous Stirred Tank Reactor (CSTR) and Tennessee Eastman Process (TEP), which achieves an average fault diagnosis accuracy of 0.983 on CSTR and 0.955 on TEP.