What question did this study set out to answer?

The aim is to predict purchase intentions for marine cultural products using a dual-stream deep learning model that combines visual and textual data.

May 24, 2026Open Access

Deep prediction of marine cultural and creative products purchase intentions by integrating visual significance and textual emotion

Key Points

The aim is to predict purchase intentions for marine cultural products using a dual-stream deep learning model that combines visual and textual data.
Developed a dual-stream deep learning model integrating visual saliency from images and sentiment analysis from text comments.
Conducted experiments on public datasets to evaluate the model's performance compared to baseline approaches.
Measured prediction accuracy and recall rates in assessing purchase intentions.
Achieved a purchase intention prediction accuracy of 85.6%, surpassing baseline models using images (72.1%) or text (78.3%).
Noted over a 10 percentage point increase in recall rate compared to traditional methods.
Demonstrated effectiveness for multimodal fusion analysis and personalized recommendations in the marine cultural sector.

Abstract

Precise prediction of the purchase intention for marine cultural and creative products is of vital importance for e-commerce marketing.Addressing the issues of the existing methods that separately analyse images and comments, and the difficulty in capturing cross-modal collaborative effects, this study proposes a dual-stream deep learning model that integrates visual saliency and text sentiment.This model achieves a deeper understanding of user preferences by simultaneously extracting the salient regions of the images and the sentiment tendencies of the comments.Experiments on public datasets show that the purchase intention prediction accuracy of this method reaches 85.6%, significantly outperforming the baseline models that only use images (72.1%) or text (78.3%), with a recall rate increase of over 10 percentage points.This study provides an effective tool for multimodal fusion analysis and personalised recommendations in the marine cultural and creative field.

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