A new traffic flow prediction model named CFHD-Former was proposed in order to address the limitations of existing traffic flow prediction models that struggle to capture dynamic variations across different time slices and cannot adequately consider the heterogeneous characteristics of traffic volume distributions across regions. An adaptive high-frequency heterogeneity module and a progressive optimization mechanism were introduced to enhance its adaptability to traffic states under different time slices. A core flow node identification module was employed building on the captured temporal heterogeneity feature in order to partition the road network into core and non-core flow networks based on nodal traffic flow characteristics. Heterogeneous modeling of the two types of road network was implemented via a spatial encoder. A frequency-domain autocorrelation MAE loss function was incorporated during backpropagation in order to consider the dependencies among different time steps within the prediction sequence, thereby reducing multi-step prediction errors. The experimental results demonstrated that the MAE of the proposed CFHD-Former model was reduced by 1.70%, 4.58% and 4.44% on the PEMS04, PEMS08 and METR-LA datasets, respectively compared with the best-performing baseline model. Results verified the effectiveness of CFHD-Former in modeling the spatio-temporal heterogeneity of complex road networks and provided a new solution for urban traffic flow prediction.