基于深度学习的技术创新治理分析,对高科技行业创新资源的智能配置进行分析
Xiaoxuan Yu1, Zhen Chen2, Xinyu Zhang3
1School of Humanities and Law, Northeastern University, Shenyang, 110169, China.
Scientific reports
|August 14, 2025
概括
深度学习 (DL) 增强技术创新治理,用于高科技行业的智能资源配置. DL模型显著优于传统方法,提高了效率和适应性.
科学领域:
- 技术创新治理 技术创新治理
- 智能资源分配 智能资源分配
- 高科技产业 高科技产业
背景情况:
- 深度学习 (DL) 为高科技行业的复杂创新资源分配挑战提供了新的解决方案.
- 技术创新的有效治理对于优化资源分配至关重要.
- 高科技产业在管理和分配创新资源方面面临着独特的障碍.
研究的目的:
- 研究基于深度学习 (DL) 的技术创新治理对智能创新资源配置的影响.
- 构建和验证一个结构方程模型,评估DL应用和治理系统的完整性.
- 为高科技企业和政策制定者提供经验证据和实际指导.
主要方法:
- 开发一个结构方程模型,将DL应用深度/宽度和治理系统完整性纳入其中.
- 在五个主要高科技行业的500家企业和15个案例研究的实证分析.
- 从多源数据构建数据集,并在高性能计算集群上进行实验.
主要成果:
- DL模型实现了0.83的平均交叉验证准确度,并且以0.88的准确度超过了传统方法.
- 敏感性分析表明,外部市场需求是影响业绩的重要因素.
- 案例研究证实,在拥有高DL采用率和强有力的治理的企业中,资源分配效率和适应能力更高.
结论:
- 基于深度学习的治理显著改善了高科技行业的智能创新资源配置.
- 在DL应用,治理系统的完整性和企业业绩之间存在很强的相关性.
- 该研究为优化创新资源管理提供了理论框架和实际见解.
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