Application of BP neural network optimized by particle swarm optimization in clothing fashion color prediction
LI Chenwei
GE Beibei
WANG Huiping
WANG Yucheng
LIU Rangtong
Abstract：Trend prediction of fashion colors plays a crucial role in the field of fashion design.However,traditional prediction methods mainly rely on existing fashion colors,often resulting in low prediction accuracy and lagging update cycles.To improve the real-time performance and reliability of prediction,an image dataset was constructed using popular clothing data released by mainstream fashion websites from January 2023 to January 2025.Image color extraction technology was employed to establish a fashion color database,and a fashion color prediction model was built based on the BP neural network optimized by the particle swarm optimization algorithm.The mean absolute error(MAE)index was used to validate the model,and the fashion color trends from February to June 2025 were predicted.Experimental results demonstrate that the PSO-BP neural network significantly outperforms the traditional BP neural network in fashion color prediction,markedly improving accuracy.The predicted results exhibit high consistency with the actual application of clothing colors.
Keywords：trend predictionfashion color datasetparticle swarm optimizationBP neural network
Publication Date：2025-12-25
Online Publishing Date：2026-01-27(First online date of this platform, not the publication date of the document)
Pages：8( 31-37,43 )