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

Abstract

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.

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