基于深度学习的电商评论多模态大数据融合与价值挖掘研究

Research on Multimodal Big Data Fusion and Value Mining of E-Commerce Reviews Based on Deep Learning

摘要: 深度学习驱动的多模态电商评论(文本、图像、视频、语音等)正在深刻重构电子商务内容生态。为探究其价值挖掘机制与伴生伦理风险，实现技术创新与消费者权益的平衡，本文以多模态大数据融合与深度学习技术逻辑为视角，系统分析其对评论功能、平台运营、消费者信任及生态位阶的重构路径，并梳理技术深化进程中出现的真实性挑战、隐私侵蚀与算法歧视等困境。研究发现，多模态评论通过“视觉证实”、社交临场感与算法精准分发，有助于提升内容生产效率、信任强度与运营智能化，可能推动评论生态从辅助系统向平台核心资产与差异化竞争优势转型；同时，AIGC伪造、过度数据采集与训练数据偏见也导致“眼见不再为实”、隐私权益失控与市场不公。基于此，本文提出以“发展与规范动态平衡”为核心原则，构建平台内生治理(真实度中台、算法透明、精细化授权)、多元共治(行业自律公约 + 外部监管红线)与人机协同(机器筛查 + 人工终审、人在回路)的三层治理框架。本研究既丰富了多模态电商评论生态的理论视角，也为平台智能升级与可持续治理提供了实践参考。

Abstract: Deep learning-driven multimodal e-commerce reviews (encompassing text, images, videos, speech, etc.) are profoundly reshaping the content ecosystem of e-commerce. To explore their value-mining mechanisms and associated ethical risks, while achieving a balance between technological innovation and consumer rights protection, this study adopts the perspective of multimodal big data fusion and deep learning technological logic. It systematically analyzes the reconstruction paths of review functions, platform operations, consumer trust, and ecosystem hierarchy, while identifying key dilemmas emerging during technological deepening, including authenticity challenges, privacy erosion, and algorithmic discrimination. The findings indicate that multimodal reviews contribute to enhancing content production efficiency, trust intensity, and operational intelligence through mechanisms such as “visual verification”, social presence, and precise algorithmic distribution, potentially propelling the review ecosystem from an auxiliary system toward a core platform asset and differentiated competitive advantage. Concurrently, AIGC-based forgery, excessive data collection, and biases in training data have led to the erosion of “seeing is believing”, uncontrolled infringement of privacy rights, and new forms of market inequity. Accordingly, this study proposes a three-tier governance framework centered on the core principle of “dynamic balance between development and regulation”: platform-endogenous governance (authenticity detection mid-end platform, algorithmic transparency, and granular authorization), multi-stakeholder co-governance (industry self-discipline conventions coupled with external regulatory red lines), and human-machine collaboration (machine screening + human final review, human-in-the-loop). This research not only enriches the theoretical perspective on multimodal e-commerce review ecosystems, but also provides practical guidance for intelligent platform upgrading and sustainable governance.