电子商务平台用户评论体系的挑战与智能化治理框架研究
Research on the Challenges and Intelligent Governance Framework of User Review System in E-Commerce Platforms
DOI: 10.12677/ecl.2026.154462, PDF,   
作者: 冯佳俊：贵州大学大数据与信息工程学院，贵州 贵阳
关键词: 电子商务；用户评论体系；智能化治理；人工智能；E-Commerce； User Review System； Intelligent Governance； Artificial Intelligence
摘要: 随着互联网技术与移动支付的快速发展，电子商务行业持续扩张，电子商务平台已成为商品交易与信息交流的重要载体。在网络购物环境中，用户评论逐渐成为消费者获取商品信息和评估服务质量的重要依据，对平台交易秩序与用户决策产生重要影响。然而，随着评论规模的迅速增长，评论体系也面临虚假评论、情绪操控、信息噪声以及内容质量参差不齐等问题，给平台治理与信息可信度带来新的挑战。传统人工审核与简单规则识别方式难以有效应对海量评论数据的复杂特征。近年来，人工智能与深度学习技术的发展为评论内容分析、情感识别与异常评论检测提供了新的技术路径。基于此，本文围绕电子商务平台用户评论体系的关键问题，分析评论生态面临的主要挑战，并探索融合人工智能技术的智能化治理框架，以提升评论信息的可信度与平台治理效率，从而促进电子商务平台生态的健康发展。
Abstract: With the rapid development of internet technology and mobile payment, the e-commerce industry continues to expand, and e-commerce platforms have become important carriers for commodity transactions and information exchange. In the online shopping environment, user reviews have gradually become an important basis for consumers to obtain product information and evaluate service quality, significantly impacting platform transaction order and user decision-making. However, with the rapid growth in the scale of reviews, the review system also faces problems such as fake reviews, emotional manipulation, information noise, and inconsistent content quality, posing new challenges to platform governance and information credibility. Traditional manual review and simple rule-based identification methods are insufficient to effectively cope with the complex characteristics of massive amounts of review data. In recent years, the development of artificial intelligence and deep learning technologies has provided new technical paths for review content analysis, sentiment recognition, and abnormal review detection. Based on this, this paper focuses on the key issues of the user review system of e-commerce platforms, analyzes the main challenges faced by the review ecosystem, and explores an intelligent governance framework integrating artificial intelligence technology to improve the credibility of review information and the efficiency of platform governance, thereby promoting the healthy development of the e-commerce platform ecosystem.
文章引用：冯佳俊. 电子商务平台用户评论体系的挑战与智能化治理框架研究[J]. 电子商务评论, 2026, 15(4): 846-852. https://doi.org/10.12677/ecl.2026.154462

参考文献

[1] 姚嘉. 电子商务发展对物流经济促进作用分析[J]. 电子商务评论, 2026, 15(2): 904-908.
[2] 罗国师. 电子商务平台中用户评论的影响与优化策略[J]. 电子商务评论, 2025, 14(8): 129-134.
[3] 于益民. 电子商务平台虚假评论治理行为动态博弈研究[D]: [硕士学位论文]. 北京: 北京外国语大学, 2024.
[4] Mudambi, S.M. and Schuff, D. (2010) What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com1. MIS Quarterly, 34, 185-200. [Google Scholar] [CrossRef] 
[5] Chevalier, J.A. and Mayzlin, D. (2003) The Effect of Word of Mouth on Sales: Online Book Reviews. NBER Working Paper 10148. [Google Scholar] [CrossRef] 
[6] Zhang, L., Zhao, J. and Xu, K. (2016) Who Creates Trends in Online Social Media: The Crowd or Opinion Leaders? Journal of Computer-Mediated Communication, 21, 1-16. [Google Scholar] [CrossRef] 
[7] Mercha, E.M. and Benbrahim, H. (2023) Machine Learning and Deep Learning for Sentiment Analysis across Languages: A Survey. Neurocomputing, 531, 195-216. [Google Scholar] [CrossRef] 
[8] 姜兴明. 在线评论对消费者网络购买行为的影响研究[J]. 电子商务评论, 2024, 13(4): 5114-5123.
[9] 赵巧. 网络环境下消费者购买决策行为影响因素分析[J]. 现代商贸工业, 2016, 37(7): 66-68.
[10] 郑小平. 在线评论对网络消费者购买决策影响的实证研究[D]: [硕士学位论文]. 北京: 中国人民大学, 2008.
[11] 贾力. 正面网络评论类型对消费者购买态度的影响研究[D]: [硕士学位论文]. 武汉: 华中科技大学, 2018.
[12] Luca, M. (2011) Reviews, Reputation, and Revenue: The Case of Yelp.Com. Harvard Business School Working Paper, No. 12-016.
[13] Zervas, G., Proserpio, D. and Byers, J.W. (2021) A First Look at Online Reputation on Airbnb, Where Every Stay Is above Average. Marketing Letters, 32, 1-16. [Google Scholar] [CrossRef] 
[14] Berger, J., Sorensen, A.T. and Rasmussen, S.J. (2010) Positive Effects of Negative Publicity: When Negative Reviews Increase Sales. Marketing Science, 29, 815-827. [Google Scholar] [CrossRef] 
[15] Pavlou, P.A. and Gefen, D. (2004) Building Effective Online Marketplaces with Institution-Based Trust. Information Systems Research, 15, 37-59. [Google Scholar] [CrossRef] 
[16] Dellarocas, C. (2003) The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms. Management Science, 49, 1407-1424. [Google Scholar] [CrossRef] 
[17] 刘冰洋, 李超, 高将军. 基于深度学习的电商评论情感分析——以手机电商评论为例[J]. 电子商务评论, 2025, 14(11): 217-227.
[18] Cambria, E., Schuller, B., Xia, Y. and Havasi, C. (2013) New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28, 15-21. [Google Scholar] [CrossRef] 
[19] McAuley, J. and Leskovec, J. (2013) Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text. Proceedings of the 7th ACM Conference on Recommender Systems, 12-16 October 2013, 165-172. [Google Scholar] [CrossRef] 
[20] Ott, M., Choi, Y., Cardie, C. and Hancock, J. (2011) Finding Deceptive Opinion Spam by Any Stretch of the Imagination. arXiv: 1107.4557.
[21] Hochreiter, S. and Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9, 1735-1780. [Google Scholar] [CrossRef] [PubMed]
[22] Koren, Y., Bell, R. and Volinsky, C. (2009) Matrix Factorization Techniques for Recommender Systems. Computer, 42, 30-37. [Google Scholar] [CrossRef] 
[23] He, X.N., Liao, L.Z., Zhang, H.W., Nie, L.Q., Hu, X. and Chua, T.S. (2017) Neural Collaborative Filtering. arXiv: 1708.05031.

为你推荐