Firm survival, as a key indicator of regional economic resilience, has gained increasing attention in the context of global economic uncertainty and the deep adjustments in industrial structures. In the Pearl River Delta (PRD), a core region of China’s Guangdong-Hong Kong-Macau Greater Bay Area, the characteristics of firm life cycles are crucial for understanding spatial development inequalities and institutional effects in emerging economies. This study focuses on firms in the PRD, using full life-cycle data from registration, operation, to deregistration. An XGBoost regression model is employed, incorporating the SHAP explanation algorithm, to systematically analyze the main factors influencing firm survival. The results show that: (1) Firm establishment time is the primary factor influencing survival, with significant “survival threshold” and “growth leap” effects—mature firms exhibit a distinct survival advantage; (2) Among spatial structure variables, moderate industry specialization and diversity enhance firm survival rates, while excessive concentration and diversification show diminishing or negative returns, reflecting an “ecological threshold” effect; (3) External shocks have a significant suppressive impact on startups, while policy support and capital size show limited explanatory power; (4) The ownership structure, especially state-holding, has a positive moderating effect on firm survival in specific contexts, indicating that private enterprises’ flexibility and adaptability can compensate for institutional gaps. This study offers insights into the spatial heterogeneity of firm survival mechanisms, providing quantitative evidence for regional economic policy adjustments and firm resilience-building. Recommendations include promoting differentiated policies, optimizing industrial ecological spatial layouts, and piloting systems to enhance survival resilience, especially for mixed-ownership firms.

把握新技术革命发展机遇,推动产业智能化升级,对于新形势下实现经济高质量发展具有重要作用。文章基于 2003—2019 年中国 283 个地级市数据和网络爬虫获得的企业微观数据构建城市产业智能化指数,采用社会网络分析方法考察产业智能化的空间关联网络特征；此外,运用机器学习中的极限梯度提升树算法（XGBoost）识别出产业智能化的核心驱动因素,在此基础上借助加权指数随机图模型（ERGM）探析产业智能化空间关联网络驱动机制。研究发现：（1）样本期间产业智能化空间关联强度不断提高,但整体处于较低水平,存在较大的提升空间；大部分城市位于网络边缘位置,主要依靠城市群或中心城市对周边城市产业智能化产生辐射带动作用。（2）XGBoost 算法测算结果表明,技术创新、产业结构升级和对外开放是产业智能化的核心影响因素,累积贡献率高达 92.72%。（3）技术创新、产业结构升级和对外开放有利于加强城市间的产业智能化合作。异质性分析发现,产业结构升级主要推动外围城市、高产业智能化水平城市形成紧密的产业智能化空间关联,而技术创新和对外开放对其驱动作用有限。据此,文章提出了推动产业智能化空间协调发展的相应政策建议。