Analysis of the packaging characteristics of creative products in the context of new media based on Bayesian networks

Abstract

Abstract Clarifying the features of the packaging of cultural and creative products is beneficial to out-of-the-loop cultural and creative products. In this paper, starting from probability theory, graph theory and information theory principles, we combine Bayesian network structure and feature selection theory to construct a feature extraction model for cultural and creative product packaging. The maximum mutual information principle is used to optimize the feature selection scoring function, and experimental analyses of execution time and performance comparisons are conducted for the BNCC-FS method. In terms of execution time, the average training time of the BNCC-FS method on the eight datasets is 4.73s, which is 14.53% and 43.44% lower compared to CC-FS and ECC-FS, respectively. In terms of performance comparison, the average HammingLoss value of the BNCC-FS method on sixteen datasets was 0.155, which was 2.52% and 10.41% lower compared to CC-FS and ECC-FS, respectively. This shows that the Bayesian network-based feature extraction method can effectively achieve feature extraction of the data and also provides a new research database for the analysis of the packaging features of cultural and creative products.