Traffic flow prediction is an essential component of environmental, safe, and efficient intelligent transportation system. Due to the powerful spatial-temporal data representation ability, spatial-temporal graph neural network is widely used in traffic flow prediction. Nevertheless, existing spatial-temporal graph neural network based traffic flow prediction models have two limitations. (1) The static topology graph constructed from city spatial correlation ignores the dynamic traffic patterns, which are unable to reflect the temporal dynamic correlation between nodes in road network; and (2) only considering the spatial correlation of local traffic areas lacks the spatial correlations between the local region and the global road network. To overcome the above limitations, this study proposes a multi-view fused spatial- temporal dynamic graph convolutional network model for traffic flow prediction. Firstly, it constructs a road network spatial structure graph and a dynamic traffic-flow association graph from the perspectives of static spatial topology and dynamic traffic patterns, and uses dynamic graph convolution to learn the node features from both perspectives, comprehensively capturing the diverse spatial correlations in the road network. After that, from the local and global perspectives, it calculates the global representation of the road network and fuses global features with local features to enhance the expressiveness of node features and explore the global structural features of traffic flow. Finally, the model designs a local convolutional multi-head self-attention mechanism to obtain the dynamic temporal correlation of traffic data, achieving accurate traffic flow prediction under multiple time windows. The experimental results on four real traffic data demonstrate the effectiveness and universality of the proposed model.