This research pioneers an intelligent subsidy governance framework for drone delivery in new retail ecosystems, addressing the critical challenge of balancing efficiency, equity, and sustainability in urban logistics. We establish a theoretical framework integrating user profiles, scenario characteristics, and policy objectives. At its core, a hybrid algorithm engine synergizes: 1) Graph Neural Networks decoding industrial chain synergies through heterogeneous knowledge graphs, 2) Reinforcement Learning enabling adaptive subsidy tuning to market volatility, and 3) Meta-learning-enhanced Collaborative Filtering overcoming cold-start limitations. The “city-enterprise-consumer” knowledge graph transforms multi-source urban data into cross-domain intelligence, facilitating precision targeting from isolated entities to networked ecosystems. Key innovations include dynamic algorithm orchestration and resilience-adaptive subsidy propagation via industrial leverage nodes. This paradigm shifts subsidy design from static fiscal allocation to computationally governed spatial-temporal adaptation, establishing a replicable template for algorithmic policy intelligence in sustainable urban logistics.