An Intelligent Design Method for Extracting Emotional Features from Product Appearance Based on Deep Learning

Qin Yun

GBP Proceedings Series, 2025, vol. 16, 135-141

Abstract: The continuous advancement of high-quality social and economic development has not only elevated material living standards but also intensified the public's demand for richer and more nuanced emotional experiences. Within this context, the emotional design of product aesthetics has become an increasingly critical aspect of contemporary product development, influencing user satisfaction, brand perception, and overall market competitiveness. This paper centers on the integration of deep learning techniques for extracting emotional features from product appearances and explores their application in intelligent design processes. Initially, it examines the theoretical foundations and practical significance of emotional design in product aesthetics, highlighting how products can convey specific feelings, moods, and user-centered experiences through visual and tactile characteristics. Subsequently, the study details systematic methodologies for the extraction of emotional features, encompassing comprehensive steps such as large-scale data acquisition, preprocessing of complex visual inputs, construction of neural network models, and iterative training strategies aimed at capturing subtle emotional cues embedded in product forms. Building upon these extracted features, the paper introduces an intelligent design framework that enables the automatic generation of product aesthetics aligned with desired emotional responses. Experimental results and case studies demonstrate that this approach significantly enhances the emotional expressiveness and communicative power of product designs, fostering stronger engagement between users and products. By bridging the gap between computational intelligence and human-centered design, this research provides innovative perspectives, methodological guidance, and practical insights for designers seeking to create emotionally resonant products in an era increasingly defined by user experience and aesthetic sensitivity.

Keywords: deep learning; product appearance; emotional feature extraction; intelligent design method (search for similar items in EconPapers)
Date: 2025
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