人工智能驱动中国智慧城市治理：技术路径、治理机制与多案例证据Artificial‑Intelligence‑Driven Urban Governance in China: Technical Paths, Governance Mechanisms and Multi‑case Evidence

Abstract

Artificial intelligence is gradually evolving from a single technological tool into an important technological force that shapes the governance structure and operating mechanisms of smart cities. This paper adopts a descriptive multi-case analysis, focusing on the implementation pathways and actual roles of artificial intelligence in China's smart city construction, and explores in depth how it reshapes urban governance models through systemic innovation. The case materials indicate that artificial intelligence, relying on the technical loop of "perception—cognition—decision-making—optimization," participates in smart city construction mainly through four types of technological pathways: first, precise urban perception based on multimodal deep learning; second, secure data fusion facilitated by federated learning; third, system optimization combining reinforcement learning and digital twins; and fourth, urban intelligent agents powered by large models. The aggregated case materials show that these technological pathways have already produced observable application results in typical smart city scenarios in China, such as a 20% reduction in traffic congestion, an increase in the proactive discovery rate of urban events to over 90%, and a 30% improvement in the accuracy of epidemic spread prediction. Based on the existing cases and literature, this paper proposes three development trends worthy of further testing: from functional intelligence to scenario intelligence, from data-driven to knowledge-and-data co-driven, and from technological empowerment to mechanism reconstruction. These trends can provide contextualized references for mechanism comparison and subsequent research on intelligent governance in megacities.

摘  要：人工智能正由单一技术工具逐步演变为影响智慧城市治理结构与运行机制的重要技术力量。本文采用多案例描述性分析，聚焦人工智能在中国智慧城市建设中的落地路径与实际作用，深入探究它如何依靠系统性创新重塑城市治理的模式。案例材料表明，人工智能依靠“感知—认知—决策—优化”的技术闭环，主要通过四类技术途径参与智慧城市建设：一是围绕多模态深度学习的城市精准感知，二是联邦学习促进的数据安全融合，三是强化学习与数字孪生结合的系统优化，四是大模型加持的城市智能体。汇总的案例资料显示，这些技术路径已经在中国典型智慧城市场景中得到可观察的应用成效，比如交通拥堵下降20%、城市事件主动发现率提高到90%以上、疫情传播预测精度提高30%。基于现有的案例与文献，本文提出三项值得进一步检验的发展趋势：从功能智能转向场景智能、从数据驱动转向知识与数据协同驱动、从技术赋能转向机制重构，可为超大城市智能治理的机制比较与后续研究提供情境化參考。