Abstract: Traditional Hierarchical Federated Learning (HFL) encounter significant challenges in real world due to device heterogeneity, data heterogeneity (e.g., variations in data volume and feature distribution), and communication resource constraints. Device heterogeneity results in inefficient cross-device collaboration during model training, whereas data heterogeneity induces accuracy degradation and diminished generalization capabilities in the global model. To address these limitations while maximizing the utilization of computational, communication, and data resources in the heterogeneous edge networks, we propose an adaptive synergistic method for hierarchical federated learning. This method synergistically integrates model partitioning and client selection under hardware resource constraints, communication bottlenecks, and non-independent and identically distributed (Non-IID) data conditions to accelerate federated learning training while enhancing model accuracy and adaptability across heterogeneous environments. To quantify the influence of local datasets on global model convergence, a data contribution metric is introduced to evaluate the consistency of client contributions. Furthermore, by integrating Deep Reinforcement Learning (DRL) with real-time resource monitoring and data contribution quantification, the DRL agent dynamically optimizes client selection and edge-cloud collaborative model partitioning strategies prior to each training iteration. This adaptive mechanism leverages system resource availability (e.g., bandwidth, device status) and local data contribution scores to derive optimal policies, thereby accelerating training convergence and enhancing global model accuracy. Simulation results demonstrate that the proposed method achieves significant improvements in model accuracy and training efficiency compared with baseline algorithms, while exhibiting robust adaptability across diverse heterogeneous environment configurations.