Intelligent Generation of Food Packaging Design Based on User's Perceptual Images of Online Reviews

ZHANG Xi; CUI Rongrong; SHEN Xinyue; ZHANG Qihan

doi:10.19554/j.cnki.1001-3563.2025.16.027

PDF(720 KB)

Packaging Engineering ›› 2025, Vol. 46 ›› Issue (16) : 332-339. DOI: 10.19554/j.cnki.1001-3563.2025.16.027

Visual Communication Design

Intelligent Generation of Food Packaging Design Based on User's Perceptual Images of Online Reviews

ZHANG Xi, CUI Rongrong, SHEN Xinyue, ZHANG Qihan

Author information +

History +

Abstract

In order to make food packaging design accurately reflect consumers' differentiated preferences and respond to dynamic market changes, the work aims to propose a method to obtain consumers' perceptual images of food packaging through data mining of consumers' online reviews, and assist artificial intelligence to accurately generate packaging design schemes. The online review data of consumers were obtained through web crawler technology, and the adjectives in the data were extracted and transformed into product perceptual evaluation image words. Based on the semantic difference method, the perceptual quality evaluation scale of the product was constructed, and the multiple regression analysis was used to confirm the user's preference for packaging design. Finally, the preference information was transformed into the generation parameters, and the preliminary scheme was generated by Midjourney for designers to optimize and improve. With honey packaging design as an example, eight honey packaging design schemes with preference differences were obtained by this method. Designers selected one of them to continue the design deepening, and used the NPS net recommendation value to evaluate the design scheme, which obtained higher consumer satisfaction and verified the effectiveness of the method. This method enables precise identification of consumers' unique preferences, facilitating designers to leverage AIGC tools more efficiently to expedite the development process, enhance design productivity, and introduce fresh concepts and methodologies for food packaging design innovation.

Key words

online review data mining / perceptual evaluation / food packaging design / artificial intelligence generated content (AIGC) aided design

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHANG Xi, CUI Rongrong, SHEN Xinyue, ZHANG Qihan. Intelligent Generation of Food Packaging Design Based on User's Perceptual Images of Online Reviews[J]. Packaging Engineering. 2025, 46(16): 332-339 https://doi.org/10.19554/j.cnki.1001-3563.2025.16.027

References

[1] 肖人彬, 林文广. 数据驱动的产品创新设计研究[J]. 机械设计, 2019, 36(12): 1-9.
XIAO R B, LIN W G.Research on Data-Driven Product Innovation Design[J]. Journal of Machine Design, 2019, 36(12): 1-9.
[2] 曹喆, 郭慧兰, 吴江, 等. 元宇宙的理想与现实: 基于评论挖掘的VR产品用户感知研究[J]. 数据分析与知识发现, 2023, 7(1): 49-62.
CAO Z, GUO H L, WU J, et al.The Ideal and Reality of Metaverse: User Perception of VR Products Based on Review Mining[J]. Data Analysis and Knowledge Discovery, 2023, 7(1): 49-62.
[3] 孙冰, 沈瑞. 基于在线评论的产品需求偏好判别与客户细分——以智能手机为例[J]. 中国管理科学, 2023, 31(3): 217-227.
SUN B, SHEN R.Online Reviews for Product Demand Preference Discrimination and Customer Segmentation: A Case Study of the Smart Phone Data[J]. Chinese Journal of Management Science, 2023, 31(3): 217-227.
[4] 王安宁, 张强, 彭张林, 等. 在线评论的行为影响与价值应用研究综述[J]. 中国管理科学, 2021, 29(12): 191-202.
WANG A N, ZHANG Q, PENG Z L, et al.A Review of Behavioral Influence and Value Application for Online Reviews[J]. Chinese Journal of Management Science, 2021, 29(12): 191-202.
[5] 冯智依. 基于LDA模型的小米12手机在线评论研究[D]. 上海: 上海师范大学, 2023.
FENG Z Y.Research on Online Review of Xiaomi 12 Mobile Phone Based on LDA Model[D]. Shanghai: Shanghai Normal University, 2023.
[6] HU M Q, LIU B.Mining and Summarizing Customer Reviews[C]//Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle: ACM, 2004.
[7] 罗向东, 强威, 张希莹, 等. 基于文本挖掘的跑鞋用户评价及情感分析[J]. 丝绸, 2024, 61(6): 108-119.
LUO X D, QIANG W, ZHANG X Y, et al.User Evaluation and Sentiment Analysis of Running Shoes Based on Text Mining[J]. Journal of Silk, 2024, 61(6): 108-119.
[8] 王雪, 董庆兴, 张斌. 面向在线评论的用户需求分析框架与实证研究——基于KANO模型[J]. 情报理论与实践, 2022, 45(2): 160-167.
WANG X, DONG Q X, ZHANG B.Analytical Framework and Empirical Study of User Needs for Online Reviews Based on KANO Model[J]. Information Studies (Theory & Application), 2022, 45(2): 160-167.
[9] XUE L, YI X, ZHANG Y.Research on Optimized Product Image Design Integrated Decision System Based on Kansei Engineering[J]. Applied Sciences, 2020, 10(4): 1198.
[10] 蔡瑞虹, 程建新, 周志勇. 云石机意象造型认知与感性设计研究[J]. 机械设计, 2018, 35(1): 121-124.
CAI R H, CHENG J X, ZHOU Z Y.Image Modeling Cognition and Kansei Design Study of Marble Machine[J]. Journal of Machine Design, 2018, 35(1): 121-124.
[11] 刘婕羽, 江影. 运动内衣后背款式设计的感性评价[J]. 针织工业, 2019(7): 64-68.
LIU J Y, JIANG Y.Perceptual Evaluation of back Style Design of Sports Underwear[J]. Knitting Industries, 2019(7): 64-68.
[12] 卢世主, 闵子怡, 黄秋雨, 等. 自然语言处理模型与文本图像生成技术驱动下的包装设计[J]. 包装工程, 2024, 45(22): 232-241.
LU S Z, MIN Z Y, HUANG Q Y, et al.Packaging Design Driven by Natural Language Processing Model and Text Image Generation Technology[J]. Packaging Engineering, 2024, 45(22): 232-241.
[13] 梁逸晟. AIGC技术在包装设计中的应用研究[J]. 绿色包装, 2024(6): 154-157.
LIANG Y S.Application of AIGC Technology in Packaging Design[J]. Green Packaging, 2024(6): 154-157.
[14] 钟子薇, 刘昊. 人工智能融入非遗文创产品视觉设计思路研究[J]. 绿色包装, 2024(3): 138-142.
ZHONG Z W, LIU H.Research on the Integration of Artificial Intelligence into Visual Design of Intangible Cultural Heritage Products[J]. Green Packaging, 2024(3): 138-142.
[15] 刘奕杉, 王玉琳, 李明鑫. 词频分析法中高频词阈值界定方法适用性的实证分析[J]. 数字图书馆论坛, 2017(9): 42-49.
LIU Y S, WANG Y L, LI M X.An Empirical Analysis for the Applicability of the Methods of Definition of High-Frequency Words in Word Frequency Analysis[J]. Digital Library Forum, 2017(9): 42-49.
[16] 高彦彬. 包装设计[M]. 重庆: 重庆大学出版社, 2021.
GAO Y B.Package Design[M]. Chongqing: Chongqing University Press, 2021.
[17] REICHHELD F F.The One Number You Need to Grow[J]. Harvard Business Review, 2003, 81(12): 46-54, 124.
[18] 李成, 徐桃. 净推荐值(NPS)的数据化解读[C]//UXPA中国2016行业文集. 深圳: 努比亚技术有限公司, 2016.
LI C, XU T.Data-Based Interpretation of Net Promoter Score (NPS)[C]//UXPA China 2016 Industry Collection. Shenzhen: Nubia Technology Co, Ltd, 2016.