Consumer Behavior Classification in Online Virtual Stores Using Emotional Intelligence

Abstract

This chapter intends to probe into the predictability of consumer behavior classification (CBC) in online virtual stores under the trend of electronic commerce (e-commerce) and provide better consumer services (CS) for online shopping. First, the recurrent neural network (RNN) is expatiated and improved; thereupon, the bidirectional long short-term memory (BiLSTM) algorithm is designed and applied to the CBC; then, the support vector machine (SVM) and naive bayes classifier (NBC) are cited, and a CBC prediction model based on multi-class machine learning (ML) algorithms is implemented. Further, the proposed model is compared with other models from the perspectives of precision, accuracy, F1, and recall; the results signify that the proposed CBC prediction model has presented a 93.95% accuracy, which is at least 4.19% higher than that of other literature algorithms; besides, the performance analysis of network data transmission synchronization reveals that the proposed algorithm outperforms other algorithms with an overall transmission throughput around 1.