Fog computing serves as an intermediary between cloud computing and end users, enhancing latency and Quality of Service (QoS) by deploying resources at the network’s edge. The challenge of efficient service placement in fog environments arises from resource heterogeneity, network instability, and task dependencies. This paper introduces a hierarchical service placement framework based on deep reinforcement learning (HA3C). The framework employs a hierarchical decision mechanism to divide the service placement problem (SPP) into two subproblems: community selection and node selection. Our approach incorporates a dynamic multi-objective reward function, introducing a Load Balancer index while optimizing service delay and resource utilization. Extensive simulations on the iFogSim platform reveal that our method significantly outperforms basic algorithms in fog node placement rate while maintaining moderate to high resource utilization. In particular, HA3C exhibits enhanced robustness and scalability in large-scale tasks and complex network topologies. The experimental results confirm the efficacy of our method in optimizing service placement decisions in fog environments.