科研管理 ›› 2025, Vol. 46 ›› Issue (3): 28-37.DOI: 10.19571/j.cnki.1000-2995.2025.03.003

• 论文 • 上一篇    下一篇

数字创新生态系统构成因素对高端装备制造智能化发展的影响分析

李梅芳,刘雨菁   

  1. 福州大学经济与管理学院,福建 福州350108
  • 收稿日期:2023-07-19 修回日期:2024-12-25 出版日期:2025-03-20 发布日期:2025-03-10
  • 通讯作者: 刘雨菁
  • 基金资助:
    福建省创新战略研究项目:“产学研深度融合下企业关键核心技术突破研究”(2022R0125, 2022—2024)。

An analysis of the impact of the constituent factors of the digital innovation ecosystem on the intelligent development of high-end equipment manufacturing

Li Meifang, Liu Yujing   

  1. School of Economics and Management, Fuzhou University, Fuzhou 350108, Fujian, China
  • Received:2023-07-19 Revised:2024-12-25 Online:2025-03-20 Published:2025-03-10

摘要: 随着新一轮科技革命的发展,数字创新生态系统的应用为高端装备制造业向智能化发展提供了机遇。本研究创新性地运用深度神经网络构建长短期记忆(LSTM)模型,通过建模与预测分析,识别评估了影响高端装备制造业智能化发展的数字创新生态系统关键因素。研究发现:数字创新生态系统对高端装备制造业的智能化发展具有显著的正向推动作用。其中,智能化设施建设、偿债能力、成长能力、科研人才比重、创新产出、创新效益、创新投入以及有效发明专利占比这8个因素对智能化发展的贡献高于其他因素,且各因素的影响程度存在显著差异。本研究揭示并详尽分析了各关键因素的作用并通过对其相对贡献度的综合评估,挖掘其对企业智能化发展水平的实际影响。本文为构建客观的数字创新生态系统逻辑框架和评价体系奠定了重要的理论依据,同时为高端装备制造企业制定智能化发展策略提供了现实参考。

关键词: 数字创新生态系统, 构成因素, 高端装备制造企业, 智能化发展, LSTM模型

Abstract:     With the advancement of the new round of technological revolution, application of the digital innovation ecosystem offers opportunities for the intelligent development of the high-end equipment manufacturing industry. This study innovatively employed deep neural networks to construct a Long Short-Term Memory (LSTM) model, and identified key factors of the digital innovation ecosystem influencing the intelligent development of high-end equipment manufacturing through modeling and predictive analysis. The findings revealed that the digital innovation ecosystem significantly promotes the intelligent development of the high-end equipment manufacturing industry. Within this ecosystem, eight factors—intelligent facility construction, solvency, growth capability, proportion of R&D personnel, innovation output, innovation benefits, innovation investment, and the proportion of effective invention patents—demonstrate a significantly higher contribution to intelligent development compared to other factors, with distinct variations in their impact levels. The study revealed and thoroughly analyzed the roles of key factors in the digital innovation ecosystem, and through a comprehensive assessment of their relative contributions, uncovered their actual impact on the enterprise′s level of intelligent development. This paper has provided important theoretical supports for constructing an objective, logical framework and evaluation system for the digital innovation ecosystem, and it will offer practical references for high-end equipment manufacturing enterprises to formulate intelligent development strategies.

Key words: digital innovation ecosystem, constituent factor, high-end equipment manufacturing enterprise, intelligent development, Long Short-Term Memory (LSTM) model