Data analysis reveals evolving design dimensions in the arts and crafts market, highlighting data-driven strategies to optimize inventory turnover and product-market matching.
The arts and crafts industry is at a critical stage of digital transformation and consumption upgrading. From the perspective of big data, this study systematically deconstructs the internal logic of market development trends through multisource data collection and core design feature extraction. Multidimensional data from 2019 to 2024 are collected, covering e-commerce platform transactions, social media interactions, design patent applications, and offline exhibition and sales records. Text mining, image recognition, semantic feature extraction, and feature quantification methods are used to identify key design elements affecting market trends. The study finds that the arts and crafts market shows accelerated style iteration, stratified consumer preferences, and diversified value evaluation. Modern interpretation of traditional patterns, innovative application of environmentally friendly materials, and digital presentation constitute three core dimensions of current design features. Based on the analysis results, a data-driven industrial innovation strategy system is proposed, including precise product design, intelligent production management, and omnichannel marketing optimization. Empirical results show that enterprises using big data analysis improve product-market matching, inventory turnover efficiency, and customer satisfaction.
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Sun et al. (2026) studied this question.
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