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在美团搞敲诈 大批人被抓

赵云合 2026-06-15 09:36
赵云合 2026/06/15 09:36

邦小白快读

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本文核心曝光了外卖平台存在恶意差评敲诈、有偿刷好评两大评价生态乱象,以及美团最新的整治举措,核心干货如下:

1. 当前乱象特征:恶意敲诈已经形成成熟灰色产业链,现在不法分子可借助AI生成虚假食安截图、批量制造虚假差评,作恶成本大幅降低,商家肉眼难以辨别真假。这类行为既有白嫖党借机索要免单赔偿,也有同行恶意竞争雇人打压对手,此外还有商家诱导刷好评、专业团队批量刷分的造假行为,彻底破坏了评价的真实性。

2. 整治成果与公众参与渠道:美团升级商家AI守护系统后,过去一年帮餐饮商家拦截被骗损失超8671万元,配合各地公安机关抓获50余名涉案人员,商家遭受恶意差评的比例较一年前下降六成。针对刷好评乱象,普通消费者可一键举报违规行为,首位提供有效举报的用户还可获得20元代金券奖励。

对于布局外卖渠道的餐饮品牌来说,本文梳理了当前外卖评价生态的风险,以及品牌可借助的平台治理支持,核心干货如下:

1. 品牌经营风险提示:外卖店铺评分直接影响平台流量分配、订单营收,评分过低甚至会被限制经营、强制清退,而评价是影响评分的核心因素。当前恶意差评敲诈已经形成产业链,还出现了AI造假的新手段,识别难度更高,不少品牌曾遭遇同行雇人恶意差评打压,对品牌经营威胁较大。

2. 可借助的平台支持:美团已经升级全链路AI防护系统,AI识别恶意差评的准确率达到99%,每月可主动拦截150万条恶意内容,品牌商家可使用事前标记拉黑恶意用户、事中风险预警、事后不限次申诉等工具主动防控风险。

3. 经营启示:靠刷好评造假冲分是短期引流手段,长期会透支消费者信任,破坏品牌口碑,品牌坚持做好产品与服务,真实经营才是长期增长的核心。

作为美团外卖的入驻卖家,本文明确了当前经营中的风险,以及平台最新的治理措施和可免费使用的防控工具,核心干货如下:

1. 风险提示:当前恶意差评敲诈已经形成成熟灰色产业链,不法分子开始借助AI生成虚假投诉凭证,商家肉眼难辨真假,一旦中招不仅会产生直接经济损失,还会拉低店铺评分,导致平台流量缩水、订单减少,甚至影响正常经营,同行也会借助这类手段恶意竞争,卖家需要提前做好防范。

2. 可使用的平台工具:美团升级的商家AI守护系统构建了全链路防护体系:事前商家可自定义标记客群、一键拉黑恶意用户、凭证举报,标记功能已在北京上海全量上线,目前已有80万商家使用拉黑功能;事中系统会对风险订单弹窗预警;事后可不限次申诉,还有商家众议、第三方判责,不合理差评处置率提升70%。

3. 治理成果:过去一年美团已帮助商家拦截被骗赔损失8671万元,商家恶意差评比例下降六成,卖家可借助工具主动防控,将更多精力投入到经营中。

对于服务外卖餐饮行业的相关工厂,以及正在推进数字化、布局电商渠道的生产类工厂,本文可带来多方面启示,核心干货如下:

1. 数字化转型的落地启示:美团依托亿级真实订单和评价数据训练AI风控模型,整合用户行为、订单特征等多维特征识别恶意行为,准确率达到99%,还构建了覆盖事前-事中-事后的全链路防控体系,这种围绕具体业务痛点、场景化落地、全链路覆盖的数字化思路,值得工厂推进数字化转型参考。

2. 商业机会挖掘:当前外卖行业对鉴别虚假内容、防控恶意经营风险有强烈需求,相关提供技术服务、供应链配套服务的工厂,可以围绕这类真实需求开发对应的产品与服务,挖掘新的增长机会。

3. 合规经营启示:不管是生产还是销售端,靠违规造假牟利都是短期行为,最终都会被治理,只有合规经营才能获得长期稳定的发展,不存在长期靠钻规则漏洞获利的空间。

针对服务外卖商家的各类服务商,本文梳理了当前商家的核心痛点、行业发展趋势与新技术应用方向,核心干货如下:

1. 商家核心痛点:外卖商家长期被恶意差评敲诈困扰,面对AI生成的虚假凭证难以自证真伪,不仅要承担直接的经济损失,还会因为店铺评分下降丢失平台流量,传统应对方式只能破财消灾,缺乏有效的主动防控手段,同行恶性竞争也进一步加剧了这类问题,商家有强烈的痛点需求未被满足。

2. 新技术应用参考:AI技术已经成功应用在黑产治理领域,美团基于亿级数据训练的AI风控模型,整合多维特征识别复杂恶意场景,准确率达到99%,可以为各类To商家服务的服务商提供技术参考,服务商可围绕商家防控恶意评价的需求开发配套增值服务。

3. 行业发展趋势:维护评价真实性、打击灰色产业链是外卖行业的必然方向,服务商只有做合规的正向服务,帮助商家合法合规经营,才能获得长期的发展空间。

对于做本地生活、电商类的平台来说,本文分享了美团治理虚假评价、维护平台生态的成熟经验,对平台运营管理有较高参考价值,核心干货如下:

1. 平台商家与用户的核心需求:合法经营的商家需要公平的经营环境,希望能有效防控恶意差评敲诈、打击刷分造假,保护自身经营权益;普通消费者也需要真实的评价参考来辅助决策,这是平台维护生态必须解决的核心问题,直接影响平台的用户信任和长期发展。

2. 可借鉴的治理做法:美团构建了AI赋能的全链路治理体系,事前给商家开放自主防控工具,事中做风险预警,事后完善申诉维权渠道,同时联合公安机关打击黑色产业链,还发动普通消费者参与全民监督,设置举报奖励,技术赋权、多主体参与、联合执法多管齐下,效果显著。

3. 风险规避提示:虚假评价会透支用户对平台的信任,彻底破坏平台评价体系的信用根基,长期会影响平台整体价值,平台需要持续整治这类乱象,守住真实数据底线,才能维持健康可持续的平台生态。

对于研究本地生活平台治理、数字经济监管的研究者来说,本文呈现了AI普及背景下平台评价生态的新问题与平台治理的新实践,核心干货如下:

1. 产业出现的新问题:AI技术普及大幅降低了黑产的作恶成本,不法分子可以利用AI生成虚假凭证开展恶意差评敲诈,目前已经形成成熟的灰色产业链;同时刷好评、恶意差评两类造假行为合谋,彻底破坏了平台评价体系的信用根基,同时侵害商家和消费者双方的合法权益,这是AI普及后平台治理出现的新问题。

2. 平台治理的新实践:美团探索出AI风控+商家赋权+联合执法的多元治理模式,通过升级AI识别能力,给商家开放事前自主防控工具,完善事后申诉维权机制,同时配合公安机关打击违法犯罪,该模式取得了显著成效,数据显示实施后商家恶意差评比例下降六成,一年帮助商家减少损失8671万元。

3. 治理启示:平台生态治理需要以技术为核心赋能,同时调动商家、消费者等多主体参与,结合司法力量打击违法犯罪,才能从源头斩断灰色产业链,维护公平的营商环境。

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Quick Summary

This article exposes two major chaotic problems plaguing the review ecosystem of food delivery platforms: extortion via malicious fake reviews, and paid review manipulation, as well as Meituan's latest regulation efforts. Key takeaways are as follows:

1. Characteristics of current chaos: Extortion via malicious reviews has evolved into a mature gray industry chain. Illegal actors now use AI to generate fake food safety screenshots and create bulk fake negative reviews, drastically lowering the cost of misconduct and making it nearly impossible for merchants to distinguish fakes with the naked eye. Perpetrators range from "free meal seekers" demanding refunds and compensation to competitors hiring bad actors to sabotage rivals. On top of that, the ecosystem is also marred by merchant-induced positive reviews and bulk rating boosting by professional teams, which completely undermines the authenticity of reviews.

2. Regulation outcomes and public reporting channels: After upgrading its AI merchant protection system, Meituan helped food service merchants block over 86.71 million yuan in fraud losses over the past year, and cooperated with local public security authorities to arrest more than 50 suspects. The proportion of merchants affected by malicious negative reviews has dropped 60% compared to one year ago. For paid review manipulation, ordinary consumers can report violations with one click, and the first user to provide valid evidence receives a 20-yuan voucher reward.

For food and beverage brands with a presence on food delivery channels, this article outlines current risks to the delivery review ecosystem and platform governance support that brands can leverage. Key takeaways are as follows:

1. Operational risk alert: A delivery store's rating directly determines platform traffic allocation and order revenue; excessively low ratings can even lead to operational restrictions or forced delisting, and reviews are the core factor shaping ratings. Today, extortion via malicious reviews already forms a complete industry chain, with new AI-powered forgery techniques making fakes far harder to identify. Many brands have fallen victim to competitor-hired malicious review attacks, posing major threats to brand operations.

2. Available platform support: Meituan has upgraded an end-to-end AI protection system, with 99% accuracy in identifying malicious negative reviews, and can proactively block 1.5 million malicious pieces of content monthly. Brand merchants can use tools including pre-emptive tagging and blocking of malicious users, in-progress risk alerts, and unlimited post-incident appeals to proactively manage risks.

3. Operational insight: Rating inflation via paid fake reviews is only a short-term traffic acquisition tactic that will erode consumer trust and damage brand reputation over time. Sustained long-term growth relies on consistent investment in quality products and services, and authentic operations.

For sellers registered on Meituan Food Delivery, this article clarifies current operational risks, the platform's latest governance measures, and free防控 tools available to sellers. Key takeaways are as follows:

1. Risk alert: Extortion via malicious reviews has formed a mature gray industry chain. Illegal actors now use AI to generate fake complaint evidence that is almost impossible for sellers to distinguish from real content with the naked eye. Falling victim not only causes direct financial losses, but also drags down store ratings, leading to reduced platform traffic, fewer orders, and even disrupted normal operations. Competitors also abuse these tactics for unfair advantage, so sellers need to take preventive measures in advance.

2. Available platform tools: Meituan's upgraded AI merchant protection system builds an end-to-end protection framework: Pre-emptively, sellers can custom-tag customer groups, block malicious users with one click, and submit evidence for reports. The tagging feature has been fully rolled out in Beijing and Shanghai, and 800,000 sellers already use the blocking function. During order processing, the system sends pop-up risk alerts. After incidents, sellers can file unlimited appeals, access peer discussions and third-party adjudication, and the disposal rate for unfair negative reviews has improved by 70%.

3. Governance outcomes: Over the past year, Meituan has helped merchants block 86.71 million yuan in fraud losses, and the proportion of sellers affected by malicious negative reviews has dropped 60%. Sellers can leverage these tools to proactively manage risks and devote more energy to core operations.

For factories serving the food delivery industry, and manufacturing factories advancing digital transformation and expanding e-commerce channels, this article offers multiple insights. Key takeaways are as follows:

1. Insight for digital transformation implementation: Meituan trains its AI risk control model on hundreds of millions of real orders and review data, integrating multi-dimensional features including user behavior and order characteristics to identify malicious behavior with 99% accuracy. It has also built an end-to-end prevention framework covering pre-incident, in-progress and post-incident stages. This digital approach, centered on solving specific business pain points with scenario-based implementation and end-to-end coverage, offers a valuable reference for factories advancing digital transformation.

2. New business opportunity挖掘: The food delivery industry currently has strong demand for fake content identification and malicious operational risk防控. Factories providing relevant technical services and supply chain support can develop targeted products and services around these real demands to unlock new growth opportunities.

3. Insight for compliant operations: Whether in manufacturing or sales, profiting from illegal fraud is only a short-term strategy that will eventually be cracked down on. Only compliant operations can deliver long-term, stable development, and there is no sustained room for profit by exploiting regulatory loopholes.

For all service providers serving food delivery merchants, this article outlines merchants' core pain points, industry development trends and new technology application directions. Key takeaways are as follows:

1. Core merchant pain points: Food delivery merchants have long been troubled by extortion via malicious negative reviews, and cannot prove their innocence against AI-generated fake evidence. This not only causes direct financial losses, but also makes merchants lose platform traffic due to lower store ratings. Traditional responses can only rely on paying off perpetrators, and lack effective proactive prevention tools. Unfair competition from peers has further exacerbated the problem, leaving merchants' strong demand for solutions unmet.

2. Reference for new technology application: AI technology has already been successfully applied to black industry governance. Meituan's AI risk control model, trained on hundreds of millions of data points, integrates multi-dimensional features to identify complex malicious scenarios with 99% accuracy, offering a technical reference for all merchant-facing service providers. Providers can develop supporting value-added services to meet merchants' demand for malicious review prevention.

3. Industry development trend: Safeguarding review authenticity and cracking down on gray industry chains is an inevitable direction for the food delivery industry. Only by providing compliant, positive services and helping merchants operate legally and compliantly can service providers achieve long-term development space.

For platforms operating in local life services and e-commerce, this article shares Meituan's mature experience in governing fake reviews and maintaining a healthy platform ecosystem, offering high reference value for platform operation and management. Key takeaways are as follows:

1. Core needs of platform merchants and users: Law-abiding merchants need a fair operating environment, and effective prevention of malicious review extortion and crackdowns on rating manipulation to protect their operational rights; ordinary consumers also need authentic reviews to inform their purchase decisions. These are core problems platforms must solve to maintain a healthy ecosystem, and directly impact user trust and long-term platform development.

2. Governance practices to learn from: Meituan has built an AI-powered end-to-end governance framework: it provides merchants with self-service prevention tools in advance, issues risk alerts during operations, improves appeal and rights protection channels after incidents, collaborates with public security authorities to crack down on black industry chains, and mobilizes ordinary consumers to participate in public oversight with reward incentives for reporting. This multi-pronged approach combining technological empowerment, multi-stakeholder participation and joint law enforcement has delivered remarkable results.

3. Risk prevention提示: Fake reviews erode user trust in platforms, completely destroy the credit foundation of a platform's review system, and undermine overall platform value in the long run. Platforms must continuously crack down on this chaos, uphold the bottom line of authentic data, and maintain a healthy and sustainable platform ecosystem.

For researchers studying local life platform governance and digital economy regulation, this article presents new problems in platform review ecosystems amid AI proliferation and new practices in platform governance. Key takeaways are as follows:

1. New emerging industrial problems: The popularization of AI has drastically reduced the cost of misconduct for black industry actors. Illegal actors can now use AI to generate fake evidence to carry out malicious review extortion, which has already formed a mature gray industry chain. In addition, the collusion between positive review manipulation and malicious negative review forgery has completely destroyed the credit foundation of platform review systems, while infringing on the legitimate rights and interests of both merchants and consumers. This is a new governance problem that has emerged following the widespread adoption of AI.

2. New platform governance practices: Meituan has explored a multi-stakeholder governance model combining AI risk control, merchant empowerment and joint law enforcement. By upgrading AI identification capabilities, providing merchants with pre-emptive self-service prevention tools, improving post-incident appeal and rights protection mechanisms, and cooperating with public security authorities to crack down on illegal activity, the model has achieved remarkable results. Data shows the proportion of merchants affected by malicious negative reviews dropped 60% after implementation, and the initiative helped merchants reduce losses by 86.71 million yuan in one year.

3. Governance insights: Platform ecosystem governance requires core technological empowerment, while also mobilizing the participation of multiple stakeholders including merchants and consumers, and combining judicial force to crack down on illegal activity. Only this approach can cut off gray industry chains at the source and maintain a fair business environment.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

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出品 | 电商头条 作者 |赵云合

面对借恶意差评、虚假投诉索要免单、高额索赔的勒索行为,商家往往百口莫辩,大多只能自认吃亏掏钱消灾。

前段时间,“嫌潮汕火锅太淡欲写5000字差评”一词条冲上热搜。起因是有游客点了400多元的菜品,吃了七成左右后,告知老板火锅不符合预期,想要写5000字的差评,老板虽清楚众口难调,最后只能无奈免单,但仍感到气愤。

有网友直言:“明摆着威胁,吃霸王餐,赤裸裸的白嫖党”。这并非个例,尤其在外卖平台上更常见。而且不止是免单,还有以差评或投诉索要赔偿的。

这背后实际暗藏一条成熟的灰色产业链,不法分子出于敲诈勒索、同行恶意竞争等目的,借助AI生图、盗图等方式捏造问题,发布虚假的负面评价敲诈商家,以此牟利。

这种行为不仅给商家造成经营损失,还干扰了用户正常的下单决策。针对这一乱象,美团持续重拳整治,已经有多名不法分子被抓。

美团打击恶意差评

全面升级“商家AI守护”能力

无疑,消费者享有评价权,不论是好评还是差评,都是个人权利。

商家也并非不能接受差评,如果是顾客真实客观给出的差评、指出对应问题,商家会进行整改。但如果是出于免单、敲诈类的恶意差评,商家也有权利拿起武器维权。

针对以恶意差评进行敲诈的行为,美团推出了新的整治工具。

近日,美团全面升级“商家AI守护”能力。这套系统,是一个基于亿级真实订单和评价数据训练的AI风控模型。

具体来看,AI风控模型整合了用户行为、订单特征、评价语义等多维特征,专门用来精准识别恶意骗赔、差评威胁、职业索赔这些越来越复杂的场景,识别准确率达到99%,每月主动拦截150万条恶意差评与威胁。

美团为什么会推出这一AI风控模型呢?为了重点整治AI敲诈的乱象。

过去,不法分子利用恶意差评非法牟利,还得向商家编一个像样的理由,比如自己投放异物、谎称未收到餐等等;

但现在,AI成为了不法分子作恶的工具,只要给AI具体的指令,就能得到合成的食安截图、投诉凭证,或者批量生成虚假差评,并对商家进行敲诈索赔。总的来说,作恶成本进一步降低了。

面对这些AI生成的“证据”,商家仅凭肉眼难以辨别真假。就算存疑,也无法证明这些“证据”有问题,长期深陷这种无力的处境。

这次美团全面升级的“商家AI守护”能力,正是强化对AI敲诈行为的精准识别与拦截。具体来看,在AI风控模型的基础上,构建了覆盖事前—事中—事后的全链路防护。

首先是事前预防,行业内首创订单级顾客管理入口,商家可利用标记、限制下单、投诉举报这三种工具,对恶意用户实施自主预防。

具体来看,标记功能支持商家建立自定义标签标记优质老客和恶意用户,多页面同步展示以做出准确判断,该功能目前已经在北京、上海全量上线,全国陆续开放。这种对客群的分层管理,能让商家从被动应对转为主动甄别风险人群。

限制下单即商家可以一键拉黑恶意用户并设置封禁时长。截至目前,已有80万商家使用功能。举报投诉即商家能凭证举报恶意用户,核实后可限制其下单、评价权限,防止持续骚扰商家。

其次是事中预警,被标记的用户下单时会有语音提醒,风险订单也会主动弹窗,引导商家留存履约凭证,提前化解纠纷,减少后续维权成本。

最后是事后维权,商家可对所有差评发起申诉,且申诉次数不限;同时,新增商家众议、小美评审第三方判责,不合理差评处置率提升70%;搭配7×24小时客服,问题解决率上涨7%,商家维权渠道完善公正。

这一全链路风控工具,能帮助商家更从容地应对各种恶意差评,降低经营成本与心理内耗,从而将更多的时间和精力投入到菜品、服务经营中。

实际上,美团外卖自2024年12月起,就启动了恶意用户治理专项。官方数据显示,过去一年,美团已帮助餐饮商家拦截被骗赔损失8671万元,月均拦截恶意差评超20万条,识别拦截恶意骗赔超31万人次,商家遭受恶意差评的比例较一年前下降六成。

与此同时,2025年美团配合各地公安机关累计查处敲诈勒索、恶意索赔类案件30起,抓获50余人。

美团的全链路治理,不只是单纯降低商家的经济损耗,更从源头斩断职业索赔、同行恶意竞争催生的灰色产业链,重塑外卖真实客观的评价生态。技术风控与公安联合双管齐下,对靠恶意差评牟利的不法分子形成有力长效震慑。

外卖平台的评价乱象层出不穷

对外卖商家而言,店铺评分至关重要。

高分店铺能获取平台自然曝光、优先参与营销活动,客源与营收同步上涨;一旦评分走低,流量会大幅缩水,订单持续减少,严重时还会被平台限制经营、强制清退。

而消费者的评价是影响店铺评分的核心变量之一,好评不仅能吸引源源不断的客源,还能提高店铺评分;而差评则将客源拒之门外,店铺评分也随之受影响。

这也是不少商家面对差评勒索时只能破财消灾的根本原因,负面评价带来的经营代价实在难以承担。

评价生态的乱象不止恶意差评敲诈,有偿刷好评同样形成成熟灰色产业链。

在日常点餐中,经常能见到商家附赠好评返现卡片,消费者按要求晒图评价就能领取小额现金或优惠券;线下到店消费时,店员也常会引导顾客写好评,以免费小吃、礼品作为交换。

这类“好评”并非出自消费者的客观评价,而是在“优惠”诱导下给出的虚假评价,无法反映真实消费体验。

此外,更有商家铤而走险,对接专业刷分团队批量灌水好评。账号背后的“顾客”实际从未进店消费,凭空捏造正面体验,彻底扰乱评价真实性。

针对刷好评的乱象,美团旗下的大众点评此前就推出了全民监督机制,消费者可以通过平台提供的一键投诉功能进行举报。对于首位提供有效投诉的消费者,平台将给予20元代金券奖励。

商家刷好评,靠造假的高分骗顾客下单,属于自欺欺人的短期引流捷径;而恶意差评要么是同行雇水军捏造问题压低对手评分抢客源,要么是白嫖党下单找茬,拿删评做筹码进行敲诈。

本质上,二者属于同一套靠虚假数据钻平台规则空子的作弊套路。不仅侵害消费者的合法权益,还摧毁了外卖市场本该有的公平交易底线。

外卖平台的评分体系,本意是帮消费者筛选靠谱商家,同时靠真实口碑倒逼商家做好餐品、服务。但现在刷好评、恶意差评的行为,联手撕碎了这份信用根基,把公平经营变成了一场数据造假的博弈。

短期之内,不法商家、职业索赔者看似占到便宜;但长久来看,这种行为不断透支消费者信任、破坏公平营商环境,最终没有任何一方能够真正受益,各类不法分子最终也将承担对应的法律责任。

健康有序的外卖市场,应当是消费者客观理性的评价、商家主动接受监督、平台守住真实数据底线,三方约束才能让评价权真正发挥作用。

注:文/赵云合,文章来源:电商头条(公众号ID:ecxinwen),本文为作者独立观点,不代表亿邦动力立场。

文章来源:电商头条

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