Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 43, 2026 No. 2
486-493

Method of linguistic sustainable group decision-making based on time series enhancement and human-machine collaboration

Zhang Qirong
Wang Biao
School of Information Science and Technology, Qiongtai Normal University, Haikou 571127, China
DOI: 10.19734/j.issn.1001-3695.2025.05.0178

Abstract

Linguistic multi-attribute group decision-making method is an important approach for sustainable assessment of complex systems. Current research has problems such as insufficient dynamic modeling capabilities and separation of subjective and objective data. Starting from effectively capturing sequential multi-cycle dynamic patterns and balancing the influence of subjective and objective data, this paper proposed a linguistic-based sustainable group decision-making method based on time series enhancement and human-machine collaboration. Firstly, aiming at the fuzziness and randomness of linguistic data, it constructed an uncertainty conversion framework combining Z-number and normal cloud model to realize the accurate conversion from linguistic terms to quantitative data. Secondly, it designed an ARIMA-LSTM hybrid time series model to capture li-near and non-linear dynamic features hierarchically, improving the accuracy of multi-cycle prediction. Furthermore, it proposed a two-layer human-machine collaboration mechanism. Through AI-assisted annotation and group consensus optimization, the workload of expert annotation was reduced and the weights of subjective and objective data were balanced. In the dynamic decision-making stage, it introduced the sliding window entropy weight method and trend correction factor to dynamically adjust attribute weights and enhance the interpretability of ranking results. Simulation experiment results show that this method is significantly superior to comparison methods in dynamic adaptability and result stability, providing effective support for sustainable group decision-making in dynamic and complex scenarios.

Foundation Support

海南省自然科学基金资助项目(624MS073)
重庆市自然科学基金创新发展联合基金资助项目(CSTB2023NSCQ-LZX0006)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.05.0178
Publish at: Application Research of Computers Printed Article, Vol. 43, 2026 No. 2
Section: Algorithm Research & Explore
Pages: 486-493
Serial Number: 1001-3695(2026)02-020-0486-08

Publish History

[2025-09-12] Accepted Paper
[2026-02-05] Printed Article

Cite This Article

张起荣, 王彪. 基于时间序列增强与人机协同的语言型可持续群决策方法 [J]. 计算机应用研究, 2026, 43 (2): 486-493. (Zhang Qirong, Wang Biao. Method of linguistic sustainable group decision-making based on time series enhancement and human-machine collaboration [J]. Application Research of Computers, 2026, 43 (2): 486-493. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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