Traffic flow forecasting, a vital task of multivariate time series prediction, has recently expanded to incorporate Points of Interest (POI) as an additional source of data. Rather than merely leveraging historical traffic flows, POIs facilitate the understanding of inherent geographical connections and potential functional interactions between nodes. However, traditional POI-based methods tend to use POI as static feature embeddings to compute functional similarity matrices, failing to consider the dynamic influence of node functionality on traffic patterns. This overlooks the reality that even regions with analogous POIs can exhibit fluctuating traffic flow trends, particularly over extended periods. In this paper, we propose the POI-based Double-deck Graph Convolution Network (PDGCN) for more nuanced traffic forecasting. To identify potential POI-based traffic patterns, we employ the spectral clustering method to group nodes with comparable POI functionalities into regions. We then devise a POI-based dynamic graph module with temporal convolution and attention mechanisms to trace the evolving relationships between traffic nodes. This novel design underpins regional features. Experiments on two real datasets demonstrate that PDGCN effectively detects dynamic functional relationships between nodes and delivers superior prediction accuracy.