Accurate traffic flow prediction can help traffic management authorities address transportation problems, but traditional traffic flow prediction methods cannot handle nonlinear data and parameter selection well. To this end, this paper proposes the Correlation Adaptive Dynamic Graph Convolutional Networks (CADGCN) for traffic flow prediction. The CADGCN consists of an attention mechanism, a correlation graph convolutional network (CorrGCN), and a deep reinforcement learning (DRL) module. Specifically, the attention mechanism enhances the ability of the CorrGCN network to effectively capture spatiotemporal correlations, thereby improving the timeliness and accuracy of the predictions. Moreover, the CorrGCN network discovers local and global dependencies to comprehensively understand traffic flow relationships between locations. It allows the method to extract spatial and temporal correlations by aggregating node features through multiple graph convolutional layers that progressively propagate the information. Finally, the DRL module is employed to adaptively adjust the adjacency matrix according to different traffic data. The experimental results indicate that the accuracy of CADGCN is superior to that of HA, ARIMA, LSTM, GRU, CNN, DCRNN, GMAN, T-GCN, STGCN, ASTGCN, STSGCN, AGCRN, STGODE, ASTTN, and DDGformer.