Brand Marketing Strategy Based on User Emotion Recognition Model of Consumer

Abstract

Using computer aided design (CAD) technology, brands can quickly create accurate product models. Consumers can browse and customize these models online through virtual reality (VR) devices to realize personalized product customization and improve brand loyalty. In this paper, a user emotion recognition model based on constrained clustering algorithm is proposed, and the user portrait is constructed to predict the consumer's consumption tendency in VR platform and realize accurate marketing. The accuracy and recall of the algorithm in this paper are better than the decision tree algorithm in the prediction of consumer consumption tendency on VR platform, and the accuracy is improved by 22.11%. Constrained clustering algorithm groups consumer data by clustering, which can better capture the characteristics and laws in each group and has strong robustness to the interference of outliers and noise data in the group. This model can analyze the user behavior and consumption tendency in VR platform, thus providing valuable consumer insight for the brand. Through this user emotion recognition model, brands can better understand consumers' needs, predict their buying behavior and formulate accurate marketing strategies.