Summary of perceived risk dimensions.

E-commerce faces challenges such as content homogenization and high perceived risk among users. This paper aims to predict perceived risk in different contexts by analyzing review content and website information. Based on a dataset containing 262,752 online reviews, we employ the KeyBERT-TextCNN model to extract thematic features from the review content. Subsequently, we combine these thematic features with product and merchant characteristics. Using the PCA-K-medoids-XGBoost algorithm, we developed a predictive model for perceived risk. In the feature extraction phase, we identified 11 key features that influence perceived risk in online shopping. During the prediction phase, the model performs excellently across different sample types in the test set, achieving a precision (P) of 84%, a recall (R) of 86%, and an F1 score of 85%. Through the model’s interpretability analysis, we find that quality, functionality, and price are key features affecting perceived risk for electronic products. In the case of skincare products, skin safety is the most critical feature. Additionally, there are significant differences in feature characteristics between high-risk samples and normal samples.电子商务领域面临内容同质化、用户感知风险偏高的挑战。本文旨在通过分析在线评论内容与网站信息，对不同场景下的用户感知风险开展预测研究。本研究依托包含262752条在线评论的数据集，采用KeyBERT-TextCNN模型从评论内容中提取主题特征；随后将所得主题特征与产品及商家特征进行融合，并基于PCA-K-medoids-XGBoost算法构建感知风险预测模型。在特征提取阶段，本研究共识别出11项影响在线购物感知风险的关键特征。在预测阶段，该模型在测试集的不同样本类型中均表现优异，精确率达84%、召回率达86%、F1分数达85%。通过模型可解释性分析可知：针对电子产品，产品质量、功能属性与价格是影响其感知风险的关键特征；而对于护肤产品，肌肤安全性则是最为核心的影响因素。此外，高风险样本与正常样本的特征分布存在显著差异。