Mechanisms and Paths for Constructing the Organic Interface between Digital Economy and Chinese-Style Modernization Based on Cognitive Mapping

IF 3.1 Q1 Mathematics
Jiaqi Zhao
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引用次数: 0

Abstract

Abstract In the process of China’s modernization, the organic articulation of the digital economy provides key support. This paper explores the articulation network paradigm of both and proposes a method for building cognitive mapping on a complex network. For the digital modernization industry, the vector representation encoding is completed by using the multi-granularity representation of this paper, and an N-gram encoder realizes the industry entity recognition. A basic idea of relationship template annotation is developed for recognizing entities, and the relationship extraction framework includes three parts: representation learning of templates, quality assessment of templates, and relationship classification model based on template quality. The regression results of the three major economic regions, namely, the eastern region, the central region and the western region, show that the level of digital economy development in the region has a more significant positive effect on the structure of manufacturing output value. The largest regression coefficient is 3.3044 for the eastern region, while the central, western and northeastern regions are 1.85567, 2.45457, and -0.13798, respectively. Through the study of cognitive mapping, the study provides an effective paradigm for Chinese-style modernization.
基于认知图谱构建数字经济与中国式现代化有机衔接的机制与路径
在中国现代化进程中,数字经济的有机衔接提供了关键支撑。本文探讨了两者的衔接网络范式,并提出了一种在复杂网络上构建认知映射的方法。针对数字化现代化产业,利用本文提出的多粒度表示完成了矢量表示编码,并利用n图编码器实现了产业实体识别。提出了基于实体识别的关系模板标注的基本思想,关系抽取框架包括模板表示学习、模板质量评估和基于模板质量的关系分类模型三个部分。对东部、中部和西部三大经济区域的回归结果表明,区域数字经济发展水平对制造业产值结构的正向影响更为显著。东部地区回归系数最大,为3.3044,中部、西部和东北地区分别为1.85567、2.45457和-0.13798。通过认知映射的研究,本研究为中国式现代化提供了一个有效的范式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied Mathematics and Nonlinear Sciences
Applied Mathematics and Nonlinear Sciences Engineering-Engineering (miscellaneous)
CiteScore
2.90
自引率
25.80%
发文量
203
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