随着互联网技术的日益发展，电商平台蓬勃兴起，网络应用的便利性、实时性和交互性显著提升。在线评论已成为用户分享购买体验、表达观点和反馈产品质量的重要途径，越来越多的消费者依赖用户评论做出购买行为。这些评论具有客观性、可信性和及时性，反映了消费者的购买体验认知。聚类算法可用于挖掘评论数据中的信息，但面对复杂评论数据，传统聚类方法难以高效准确的提取关键信息。因此，如何利用聚类算法提取用户评论中大量且分散的购买体验认知，为商家提供精准营销建议并优化用户购买体验，具有重要研究价值。

       本文综合运用文本挖掘和聚类分析等方法，对用户评论中的购买体验认知展开研究。首先针对传统K-Means和AP聚类算法在提取购买体验认知时存在的不足，分别构建了SECF-K-Means和SECF-AP两种聚类算法；然后通过UCI标准数据集和评论数据集进行实验测试，验证聚类算法的聚类性能和实用性；最后应用于京东商城的华为手机用户评论数据，从产品、服务和情感三个方面挖掘用户购买体验认知信息，为商家提供有针对性的策略建议。研究结论如下：（1）在UCI数据集和用户评论数据的实验中，SECF-K-Means聚类算法在聚类结果、评价指标相较传统K-Means算法均有一定提升；在主题提取方面，SECF-K-Means算法去除了一些无意义并且比重比较大的干扰信息，得到的购买体验认知主题更加清晰；（2）SECF-AP聚类算法在同样的实验中较传统的AP算法聚类性能也得到了一定提升；在主题提取方面，SECF-AP算法有效减少了聚类主题数量，去掉了一些重复或不太有代表性的主题分类；（3）对比两种算法在京东商城华为手机用户评论中的实例应用效果，SECF-AP算法在该任务中整体聚类表现更优；（4）应用SECF-AP算法挖掘华为手机评论中用户购买体验认知，聚类结果显示用户产品体验认知主要集中在功能性能与外观设计，服务体验认知聚焦于物流与客服，情感体验认知体现为波折与满足、期待与释放等情绪变化。（5）为商家提出三点提升用户购买体验策略建议：一是强化屏幕质量管控，巩固手机功能与性能优势；二是优化物流与库存管理，加强客服团队建设；三是改进抢购与预约机制，打造情感化购买体验。

With the rapid development of internet technology, e-commerce platforms have flourished, significantly enhancing the convenience, real-time responsiveness, and interactivity of online applications. User Reviews have become a crucial way for users to share their Shopping Experience, express opinions, and provide feedback on product quality. Increasingly, consumers rely on User Reviews to make purchase decisions. These reviews are characterized by objectivity, credibility, and timeliness, reflecting consumers’Cognition of their purchasing experiences. Clustering Algorithms can be used to mine information from review data; however, traditional clustering methods struggle to efficiently and accurately extract key insights from complex reviews. Therefore, exploring how to use Clustering Algorithms to extract the large volume of scattered Cognition from User Reviews and provide businesses with precise marketing suggestions and optimized shopping experiences holds significant research value.

This paper integrates text mining and Clustering Analysis methods to study Cognition within User Reviews of Shopping Experiences. It first addresses the shortcomings of traditional K-Means and Affinity Propagation (AP) clustering algorithms by proposing two improved models: SECF-K-Means and SECF-AP. Experiments were conducted using UCI benchmark datasets and actual review data to validate the clustering performance and practical value of the proposed algorithms. Finally, these methods were applied to User Reviews of Huawei smartphones on JD.com, extracting Cognition of Shopping Experience from three aspects: product, service, and emotion, to offer targeted strategic suggestions to merchants.The research findings are as follows:(1) In experiments using UCI datasets and review data, the SECF-K-Means algorithm showed improved clustering results and evaluation metrics compared to traditional K-Means. It also filtered out high-frequency but meaningless noise, resulting in clearer Cognition themes.(2) The SECF-AP algorithm also demonstrated improved clustering performance over traditional AP and reduced the number of redundant or less representative clusters.(3) In the application to Huawei smartphone User Reviews, SECF-AP outperformed SECF-K-Means in overall clustering effectiveness.(4) Applying SECF-AP to extract Cognition from User Reviews revealed that product experience was focused on functionality and design, service experience on logistics and customer service, and emotional experience showed patterns of frustration and satisfaction, anticipation and relief.(5) Three strategic suggestions were made for improving the Shopping Experience: enhancing screen quality control to solidify product advantages, optimizing logistics and customer service, and improving the purchase and reservation process to create a more emotionally engaging experience.

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