RESEARCH PAPERS

Analysis on Knowledge Mutation Mechanism of Digital-Intelligent Innovation Ecosystem Based on Cusp Catastrophe Theory

  • Chu Jiewang ,
  • Zhang Zifang
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  • School of Management, Anhui University, Hefei 230601
Chu Jiewang, Professor, PhD, Doctoral Supervisor; Zhang Zifang, Master’s Candidate, Corresponding Author, E-mail: 2060731139@qq.com.

Received date: 2025-06-24

  Revised date: 2025-10-10

  Online published: 2026-06-09

Supported by

This work is supported by the National Social Science Fund of China project titled “Research on Knowledge Generation Power, Diffusion Logic and Governance Mechanism of Digital Intelligence Innovation Ecosystem” (Grant No. 23BTQ055).

Abstract

[Purpose/Significance] Given contemporary society’s strong demand for digital intelligence technology and knowledge management, the issue of ‘knowledge variation’ within the ‘digital intelligence innovation ecosystem’ has become increasingly important. Consequently, research on knowledge variation mechanisms aimed at ‘knowledge creation’ has become more prominent. [Method/Process] From the dual perspectives of knowledge evolution theory and social epistemology, this study explores the intrinsic logic of knowledge variation in the digital intelligence innovation ecosystem from both dominant logic and constructivist logic. Subsequently, based on cusp catastrophe theory, a cusp catastrophe model of knowledge variation is constructed. This model uses the degree of knowledge entropy within the ecosystem as the state variable, and the external information flow that forms new knowledge and the coherent effects among internal elements as control variables. Finally, the study conducts an empirical analysis of the model. [Result/Conclusion] The study reveals that in the digital intelligence innovation ecosystem, knowledge generation undergoes abrupt state change. The process of catastrophic knowledge generation is a process in which knowledge elements experience giant fluctuations from nonlinear interactions reinforced by sufficient information within the system. The process is characterized by abruptness and randomness, and can be divided into three stages: triggering, evolution, and stabilization.

Cite this article

Chu Jiewang , Zhang Zifang . Analysis on Knowledge Mutation Mechanism of Digital-Intelligent Innovation Ecosystem Based on Cusp Catastrophe Theory[J]. Library and Information Service, 2026 , 70(12) : 79 -91 . DOI: 10.13266/j.issn.0252-3116.2026.12.007

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