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.