Product perceptual design optimization model based on BP neural network

Abstract

Read online

BackgroundUser reviews of online shopping platforms can truly reflect users’ feelings about the use of products. They have the advantages of large sample size, wide range and uniform distribution, and can help optimize the perceptual model of product design.MethodsThe web crawler crawls user comments, and TFIDF-SP quantifies them. The principal component analysis method selects the perceptual evaluation index, the morphological analysis method disassembles the product into several main structures, and the BP neural network constructs the perceptual optimization model.ResultsTaking the paint tray as an example, according to the trained BP neural network, the shape factor combination with the highest perceptual evaluation value is predicted. The experimental results verify the accuracy of the model.ConclusionThe model based on BP neural network has the ability to quickly and accurately combine the best form factors, improves the design efficiency of product design perceptual optimization, improves the rationality of product design, and provides a new idea for consumer demand market-oriented product design.

Keywords

• kansei engineering
• BP neural network
• algorithm model
• product design optimization
• paint tray