Construction of Product Shape Sensory Imagery Prediction Model Based on QTTI and BPNN

ZHAO Xiang; YAN Maokai; LI Zhenghui; LYU Hanlu

doi:10.19554/j.cnki.1001-3563.2025.20.016

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Packaging Engineering ›› 2025, Vol. 46 ›› Issue (20) : 170-183. DOI: 10.19554/j.cnki.1001-3563.2025.20.016

Industrial Design

Construction of Product Shape Sensory Imagery Prediction Model Based on QTTI and BPNN

ZHAO Xiang¹, YAN Maokai¹, LI Zhenghui¹, LYU Hanlu^2,*

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Abstract

The work aims to propose a predictive model for perceptual imagery of product shapes with a case study on hair dryers so as to fulfill consumers' expectations for product shapes to convey emotional imagery and address issues such as the narrow scope of perceptual vocabulary collection, low efficiency, and suboptimal accuracy of emotional prediction models in traditional research on product shape imagery. The Quantitative Type-Token TheoryⅠ (QTTI), the Whale Optimization Algorithm (WOA), and the Seagull Optimization Algorithm (SOA) were applied to optimize the Backpropagation Neural Network (BPNN; WOA-BPNN; SOA-BPNN) for exploring the emotional imagery conveyed by product shapes. Firstly, product samples and consumer review texts were crawled from e-commerce platforms based on the Python programming language, and representative perceptual vocabularies were extracted from the review texts using the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and Word2vec. Secondly, morphological analysis was adopted to deconstruct the shapes of the samples to obtain the project and category design elements, and the perceptual imagery of consumers was evaluated based on the Semantic Differential Scale. Finally, a correlation model between the design elements of product shapes and users' perceptual imagery was constructed based on QTTI, BPNN, WOA-BPNN, and SOA-BPNN, and the accuracy was compared using the error method. The results indicated that the WOA-BPNN perceptual prediction model had the best accuracy. In conclusion, this method can provide guidance for product designers in shaping their design directions and offer reference ideas for researchers in related fields.

Key words

product shape / Kansei imagery / QTTI / WOA-BPNN / SOA-BPNN

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ZHAO Xiang, YAN Maokai, LI Zhenghui, LYU Hanlu. Construction of Product Shape Sensory Imagery Prediction Model Based on QTTI and BPNN[J]. Packaging Engineering. 2025, 46(20): 170-183 https://doi.org/10.19554/j.cnki.1001-3563.2025.20.016

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