Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29–31, 2024, Wuhan, China

Research Article

A Study on Convolutional Neural Network Prediction Method for Consumer Purchasing Behavior in Digital Economy

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  • @INPROCEEDINGS{10.4108/eai.29-3-2024.2347435,
        author={Wenfang  Yang and Fu  Luo and Danping  Lin},
        title={A Study on Convolutional Neural Network Prediction Method for Consumer Purchasing Behavior in Digital Economy},
        proceedings={Proceedings of the 3rd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2024, March 29--31, 2024, Wuhan, China},
        publisher={EAI},
        proceedings_a={ICBBEM},
        year={2024},
        month={6},
        keywords={neural network (cnn) models random forest multilayer perceptron (mlp)},
        doi={10.4108/eai.29-3-2024.2347435}
    }
    
  • Wenfang Yang
    Fu Luo
    Danping Lin
    Year: 2024
    A Study on Convolutional Neural Network Prediction Method for Consumer Purchasing Behavior in Digital Economy
    ICBBEM
    EAI
    DOI: 10.4108/eai.29-3-2024.2347435
Wenfang Yang1,*, Fu Luo1, Danping Lin1
  • 1: Guangdong University of Science and Technology
*Contact email: 472598243@qq.com

Abstract

This paper explores the application of deep learning techniques and their effectiveness in predicting consumer purchase behavior in the context of digital economy by constructing and evaluating convolutional neural network (CNN) models. The study is centered around a fictitious consumer behavior dataset, and describes in detail the experimental design, the model training process, the selection of performance evaluation metrics, and the comparative benchmarks with traditional machine learning methods. Through a series of fictitious experimental results, we find that the CNN model outperforms traditional models such as logistic regression, random forest, and multilayer perceptual machine (MLP) in several performance metrics, especially in terms of accuracy and F1 score. In addition, this study discusses the contribution of CNN models in terms of prediction accuracy and efficiency, as well as potential directions for future research, including model optimization, multimodal data fusion, and testing of real-world application scenarios.