Research on the Evaluation of Regional Scientific and Technological Innovation Capabilities Driven by Big Data

被引：0

作者：

Liang, Kun ^{[1
]}

Wu, Peng ^{[1
]}

Zhang, Rui ^{[1
]}

机构：

[1] Anhui Univ, Sch Business, Hefei 230601, Peoples R China

来源：

SUSTAINABILITY | 2024年 / 16卷 / 04期

关键词：

RSTI; big data; LDA; AHP-SMO; DEVELOPMENT PROJECT-SELECTION; SYSTEM;

D O I：

10.3390/su16041379

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Scientific and technological innovation (STI) is an important internal driver of social and economic development. Reasonable evaluation of regional scientific and technological innovation (RSTI) capability helps discover shortcomings in the development of urban development and guides the allocation of scientific and technological resources and the formulation of policies to promote innovation. This paper analyzes new opportunities created by big data and artificial intelligence for the evaluation of RSTI capability, and based on this analysis, the collaborative evaluation schemes of multi-entity participation are investigated. In addition, considering the important value of unstructured data in evaluating STI, the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis method are employed to analyze the construction of an evaluation indicator system that integrates scientific and technological news data. To fully utilize the respective advantages of human experts and machine learning in the field of complex issue evaluation, this paper proposes an RSTI capability evaluation model based on AHP-SMO human-machine fusion. This study promotes the integration of science and technology and economy and has theoretical and practical significance.

引用

页数：22

共 40 条

[21]

Ou X., 2017, 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017), P1430

[22]

Paredes-Frigolett H., 2014, Ranking the performance of national innovation systems in the Iberian Peninsula and Latin America from a neo-Schumpeterian economics perspective

[23]

Polednikova E., 2014, P ECMLG 2014 10 EUR

[24]

Porter M. E., 1999, The New Challenge to Americas Prosperity: Findings from the Innovation Index

[25]

Ramirez-Hernandez Luis Fernando, 2019, Journal of Technology Management & Innovation, V14, P44

[26] Modelling reference evapotranspiration using principal component analysis and machine learning methods under different climatic environments [J].

Raza, Ali ;

Saber, Kouadri ;

Hu, Yongguang ;

L. Ray, Ram ;

Ziya Kaya, Yunus ;

Dehghanisanij, Hossein ;

Kisi, Ozgur ;

Elbeltagi, Ahmed .

IRRIGATION AND DRAINAGE, 2023, 72 (04) :945-970

[27]

Navarro-Machado VR, 2016, MEDICC REV, V18, P34, DOI 10.37757/MR2016.V18.N1-2.7

[28] The link between countries' economic and scientific wealth has a complex dependence on technological activity and research policy [J].

Rodriguez-Navarro, Alonso ;

Brito, Ricardo .

SCIENTOMETRICS, 2022, 127 (05) :2871-2896

[29] RECENT PROGRESS IN MODELING RESEARCH-AND-DEVELOPMENT PROJECT-SELECTION PROCESSES [J].

SCHMIDT, RL ;

FREELAND, JR .

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, 1992, 39 (02) :189-201

[30]

Stankovic JJ, 2021, Multiple criteria decision making beyond the information age, P69, DOI [10.1007/978-3-030-52406-7_3, DOI 10.1007/978-3-030-52406-7_3]

← 1 2 3 4 →