Traffic flow prediction based on road network is of great significance in logistics transportation planning and traffic management dispatch. Under the dual influence of spatial and temporal dependency factors, how to improve the accuracy of traffic flow prediction has become a current research hotspot. However, existing studies mostly use static graph structures to capture spatial dependency, ignoring the dynamic implicit graph information contained in traffic flow, resulting in insufficient spatial information learned by graph models and ignoring the temporal dependency impact of traffic flow. Therefore, this paper proposes a new traffic flow prediction model (IDIGI) that integrates dynamic implicit graph information. First, the model constructs a dynamic implicit graph based on time-segmented sensor traffic flow embedding representation, which is then integrated with topological graph to form a dynamic fusion graph; Secondly, it extracts the temporal dependency of traffic flow using down-sampling sequences; Finally, it compares favorably with six baseline models on four real datasets, significantly outperforming existing graph prediction models.