【摘要】 【目的】响应顶层设计培育“人工智能+”低空经济领军企业的呼吁，揭示各类科技型企业孵化的要素协同机制，为“人工智能+”低空经济企业申报与培育科技型称号提供理论与实践指引。【方法】选取国内同时涵盖人工智能和低空经济概念的141家上市公司为样本，基于2021—2024年的样本数据，运用模糊集定性比较分析（fsQCA）、必要条件分析（NCA）与机器学习方法，探究企业申报高新技术企业、企业技术中心、专精特新企业、制造业单项冠军企业、工程技术研究中心和技术创新示范企业共6类科技型称号的必要条件和组态路径。【结果】(1)要素层面，fsQCA和NCA表明，单一要素不构成任一称号类型企业培育的必要条件，通过路径共现对比得知，知识产权与企业资质在路径中的核心驱动性显著，机器学习验证了所有要素的单一不必要性以及知识产权和企业资质的相对重要性及协同驱动性；(2)路径和企业层面，运用fsQCA识别出企业技术中心形成了2条孵化路径，高新技术企业演化出4条按核心驱动要素可归并为一类的孵化路径，且与技术中心路径存在组态替代效应。专精特新企业、制造业单项冠军企业、技术创新示范企业和工程技术研究中心均各演化出一条组态路径，前三者的培育路径趋同，其要素构成、典型案例和案例数量均完全一致。【结论】“人工智能+”低空经济企业实现科技型转型升级，需以知识产权与企业资质构筑技术优势，通过内外部资源协同联动，并结合权变因素开展动态适配，依托等效孵化路径实现多元科技称号的培育与申报。更多还原

【Abstract】 [Objective] To respond to the call of top-level design to cultivate leading enterprises in the “AI+” low-altitude economy, this study reveals the synergistic mechanisms of factors in the incubation of various types of technology-based enterprises, thereby providing theoretical and practical guidance for the application and cultivation of technology-based enterprise designations among “AI+ ” low-altitude economy enterprises. [Methods] A sample of 141 domestic listed companies simultaneously involving both artificial intelligence and the low-altitude economy was selected. Based on sample data from 2021 to 2024, fuzzy-set qualitative comparative analysis(fsQCA), necessary condition analysis(NCA), and machine learning methods were employed to investigate the necessary conditions and configurational pathways for the application of six types of technology-based enterprise designations: high-tech enterprises(HTE), enterprise technology centres(ETC), specialized-refined-distinctive-innovative enterprises(SRDI), manufacturing singlechampion enterprises(MSCE), engineering technology research centres(ETRC), and technological innovation demonstration enterprises(TIDE). [Results](1) At the element level, fsQCA and NCA indicated that no single element constituted a necessary condition for the cultivation of any designation type. Through comparison of path co-occurrence, it was found that intellectual property and enterprise qualifications played significant core driving roles in the pathways. Machine learning validated the non-necessity of all individual elements, as well as the relative importance and synergistic driving effects of intellectual property and qualifications.(2) At the pathway and enterprise level, fsQCA identified that ETC formed two incubation pathways, while HTE evolved four incubation pathways that could be grouped into one category according to core driving elements, and exhibited a configurational substitution effect with ETC pathways. SRDI, MSCE, TIDE, and ETRC each evolved one configurational pathway. The cultivation pathways of the first three tended to converge, with identical element composition, typical cases, and case numbers. [Conclusion] “AI+ ” low-altitude economy enterprises need to achieve technology-based transformation and upgrading by constructing technological advantages with property rights and qualifications, coordinating internal and external resources, and carrying out dynamic adaptation in conjunction with contingency factors, relying on equivalent incubation pathways to realize the cultivation and application of diversified technology-based designations.更多还原