Design and optimisation of museum cultural and creative products based on cultural big data analysis

Museum cultural and creative products often fail to meet audience expectations, leading to low appeal and homogeneity.This study employs a data-driven approach, using NLP and machine learning to analyse 680,000 visitor comments from a provincial museum.Findings show 42% of visitors prefer 'novel, culturally rich' products, yet satisfaction with existing items is low (3.7/5).Data analysis identified the 'bronze ware gluttonous pattern' and 'ancient book calligraphy' as the most valued cultural symbols.Using K-means clustering and A/B testing, new product prototypes were developed.Market tests with 1,000 participants showed a 32% rise in preference, a 26% increase in payment willingness, and a cultural perception score of 4.3.This method effectively enhances product cultural depth and market appeal, offering a viable path for revitalising traditional culture.