为探究影响顾客对生鲜物流服务质量评价的因素，提出并建立基于在线评论情感分析与LDA（latent Dirichlet allocation）相结合的物流服务质量评价模型，构建一种融合多头自注意力机制和双向长短期记忆网络（bidirectional long short-term memory network, BiLSTM）的卷积神经网络（convolutional neural network, CNN）模型（CNN-BiLSTM-Attention）对在线评论进行情感分析，并针对分类后的正负面评价进行LDA主题建模，挖掘顾客对生鲜产品物流服务需求的关注重点，得出影响生鲜物流服务质量评价的关键因素。通过Python编程实现了基于CNN-BiLSTM-Attention的情感分析，并与支持向量机（SVM）、CNN、BiLSTM和CNN-BiLSTM对在线评论进行情感分析的结果进行比较，对比结果分析发现，相较于其他模型的分类结果，CNN-BiLSTM-Attention模型在准确率、精确度、召回率、F₁等指标上均较优，有效提高了文本情感分类的准确率。研究成果表明，基于在线评论数据对生鲜电商物流服务质量的影响因素进行研究，可帮助电商企业更好地从消费者需求出发提升物流效率、改善服务质量。

To explore the factors affecting customers' evaluation of fresh logistics service quality, a logistics service quality evaluation model was proposed and established based on sentiment analysis of online reviews and latent Dirichlet allocation (LDA). A convolutional neural network (CNN) model integrating a multi-head self-attention mechanism and bidirectional long short-term memory network (BiLSTM) was constructed for sentiment analysis of online comments. Additionally, LDA topic model was carried out for positive and negative comments after classification. The key factors affecting the evaluation of fresh product logistics service quality were obtained by exploring the focus of customers' demand for fresh product logistics service. The sentiment analysis based on CNN-BiLSTM-Attention was implemented through Python programming, and the results of sentiment analysis on online comments were compared with those of support vector machine (SVM), CNN, BiLSTM, and CNN-BiLSTM. The comparison results show that, compared with the classification results of other models, the CNN-BiLSTM-Attention model is superior in accuracy, precision, recall rate, F₁, and other indexes, effectively improving the accuracy of text emotion classification. The research results demonstrate that researching the factors affecting the logistics service quality of fresh e-commerce based on online review data can help e-commerce enterprises better improve logistics efficiency and service quality from the perspective of consumer demand.