A Quantitative Model of the Industrial Design Cognitive Process Based on Information Entropy

Jiayi Zhou1*
1 Swinburne College of Shandong University of Science and Technology, Jinan, Shandong, China
* Corresponding author: Jiayi Zhou. Email: 18514778188@163.com
Journal of Digital Frontier 2026, Vol. 1, No. 1, pp. 83-105
DOI: 10.67541/jdf2604
Received: 5 June 2026; Revised: 13 July 2026; Accepted: 30 July 2026; Published: 15 August 2026
Abstract

The cognitive process of industrial design is characterized by high complexity and dynamic evolution, and its information change law has long lacked effective mathematical quantitative means. In this study, information entropy theory is introduced into the field of industrial design, and a cognitive process quantification model covering data representation, multi-dimensional entropy calculation, dynamic weight optimization, and time series state prediction is constructed. By integrating the structural information, graphic features, parameter adjustments, and time evolution of the design scheme into the information uncertainty analysis framework, three core indexes of scheme diversity entropy, structural change entropy and parameter adjustment entropy and their dynamic fusion methods are proposed. According to the phase-transition characteristics of the design process, an adaptive entropy weight optimization algorithm driven by feature difference degree is designed, and the Transformer time series prediction model is introduced to realize the automatic identification of the design phase. The experiments are verified based on a process dataset of 80 design cases covering four categories of transportation, smart equipment, home products and industrial equipment. The results show that the stage recognition accuracy of the dynamic information entropy model reaches 93.6%, which is 11.2 percentage points higher than that of the traditional Shannon entropy model, and the mean square error (MSE) and mean absolute error (MAE) are reduced to 0.067 and 0.102 respectively. The ablation experiments further verify that the time evolution module contributes most significantly to the model performance. The research demonstrates that information entropy can effectively quantify the evolution of information from decentralization to centralization in the process of design cognition, and provides a computable analysis framework for the understanding, evaluation and optimization of design process.

Keywords
Industrial design Quantification of cognitive processes Information entropy Dynamic weight optimization Time series prediction
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© 2026 The Author(s). Published by Digital Intelligence Press Limited. This is an open access article under the CC BY 4.0 license.

How to cite: Zhou, J. (2026). A Quantitative Model of the Industrial Design Cognitive Process Based on Information Entropy. Journal of Digital Frontier, 1(1), 83-105. https://doi.org/10.67541/jdf2604