Research on Big Data and Artificial Intelligence Empowering Emotional Design of Industrial Product

DOI: https://doi.org/10.62517/jbdc.202501432

Author(s)

Wei Zhu, Ying Lv*, Kai Gong

Affiliation(s)

School of Mechatronics Engineering, Lingnan Normal University, Zhanjiang, Guangdong, China *Corresponding Author

Abstract

Driven by consumption upgrading and tech innovation, industrial product design shifts from "function satisfaction" to "emotional resonance". Emotional design, key to improving user experience and competitiveness, faces bottlenecks (hard-to-quantify emotional needs, vague design-emotion correlation, low personalized efficiency), which big data and AI help solve. Taking household humidifiers as the sample, this paper explores big data and AI’s application in emotional design. It clarifies research deficiencies via literature review, builds a "demand mining - element mapping - intelligent generation & optimization - verification" technical route (integrating multi-modal data, machine learning, generative AI), and verifies effectiveness through empirical analysis. Results show big data and AI enable accurate emotional need identification, intelligent element matching, and efficient personalized schemes, supporting industrial product emotional design. Its innovation is a "multi-modal data - emotional model - intelligent design" closed-loop system, solving traditional design’s subjectivity and low efficiency, and providing a reusable methodology.

Keywords

Big Data; Artificial Intelligence (AI); Industrial Products; Emotional Design; AIGC; Kansei Engineering

References

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