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<p>This paper explores the application of deep learning in consumer purchase intention prediction, aiming to address the limitations of traditional methods in dealing with high-dimensional, nonlinear, and dynamically changing data. The study proposes a deep learning model based on a multimodal self-attention mechanism, which integrates multimodal data such as user behaviour data, social media interactions, product images, and sentiment analysis results. The model dynamically adjusts feature weights through the self-attention mechanism, combines time series modelling and sentiment analysis, and improves prediction accuracy and generalization ability. The experimental results show that compared with the baseline model, the proposed model has significantly improved in accuracy, precision, recall, F1 value, and AUC, verifying the effectiveness and superiority of the model.</p>

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Application of deep learning in consumer purchase intention prediction

  • 期刊论文

  • 发表日期：

    2025年5月20日

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  • Wenjie Si

摘要

This paper explores the application of deep learning in consumer purchase intention prediction, aiming to address the limitations of traditional methods in dealing with high-dimensional, nonlinear, and dynamically changing data. The study proposes a deep learning model based on a multimodal self-attention mechanism, which integrates multimodal data such as user behaviour data, social media interactions, product images, and sentiment analysis results. The model dynamically adjusts feature weights through the self-attention mechanism, combines time series modelling and sentiment analysis, and improves prediction accuracy and generalization ability. The experimental results show that compared with the baseline model, the proposed model has significantly improved in accuracy, precision, recall, F1 value, and AUC, verifying the effectiveness and superiority of the model.