Abstract: aspect-based sentiment analysis (ABSA) is one of the most critical tasks in natural language processing (NLP). The sentiment information can be extracted from the text in order to identify the aspect terms, opinion terms, and their sentiment polarities. Conventional ABSA models can focus mainly on the scenarios where the aspect terms are explicitly mentioned in review sentences. However, the significant limitations are confined to the implicit aspect terms, even though the relevant opinion terms can convey the sentiment toward unstated aspects. Furthermore, the opinion terms can often contain implicit feature information about the aspects. There are also domain-specific linguistic patterns in the specialized contexts, such as the grape e-commerce reviews. Some additional challenges have posed on the model's comprehension of the sentences. In this study, an advanced ABSA model, termed DMP-BTL-DA, was proposed to integrate a pre-trained language model (BERT) with a Dual-Encoding Layer, a Dual-Channel Attention mechanism, a Dynamic Prompt Layer, and a Conditional Random Field (CRF) layer. The architecture was specifically designed to capture the implicit aspect-opinion relationships and domain-specific sentiment expressions in the grape e-commerce reviews. The BERT was selected as the embedding layer to generate the contextualized word representations, particularly for robust semantic encoding. The Dual-Encoding Layer, composed of a 1D convolutional neural network (CNN) and a Recurrent Convolutional Neural Network (RCNN), was used to extract both local and global sequential features. The Dual-Channel Attention mechanism was combined with the standard attention and linear attention in order to fuse the contextual features, thus facilitating the progressive transmission of the three-dimensional sentiment information (aspect, opinion, and polarity). The mechanism was explicitly simulated to show the relationships among implicit aspect terms, opinion terms, and their sentiment polarities. To further enhance the domain adaptability, the Dynamic Prompt Layer was constructed into the customized prompt templates for the different review sentences, thereby incorporating the domain-specific knowledge (e.g., grape-related attributes like taste, quality, price, and packaging). The implicit aspects and their sentiments were recognized to improve the performance in the e-commerce grape review domain. Finally, the CRF layer was used to optimize the sequence labeling for the aspect and opinion term extraction, in order to realize the coherent and contextually appropriate predictions. A dataset of 18 984 Chinese grape e-commerce reviews was annotated with the aspect terms, opinion terms, and sentiment polarities. Experimental results demonstrate that the DMP-BTL-DA model achieved optimal performance, particularly in the ABSA task of Chinese grape e-commerce reviews. Precision values of 86.3%, 86.4%, and 83.4% were found for the aspect term extraction, opinion term extraction, and sentiment polarity classification, respectively; Recall values of 92.5%, 90.1%, and 89.4%; and F1 scores of 89.3%, 87.3%, and 86.3%. Additionally, several key insights into consumer sentiment trends were observed in the grape e-commerce. Taste and quality were the most frequently mentioned aspects, thereby reflecting the consumer priorities. Positive sentiments were strongly associated with the mentions of the taste, price, cost-performance, transportation, and packaging, thus fully meeting these attributes. Negative sentiments predominantly arose about quality or weight issues, indicating enhanced customer satisfaction. Some insights were gained for the e-commerce platforms and grape sellers, in order to optimize the product descriptions, logistics, and quality control. Moreover, the implicit aspects and domain-specific expressions greatly contributed to the broader field of agricultural product e-commerce, particularly to the data-driven decision-making on the market strategy refinement. In summary, the ABSA was realized on the implicit aspect recognition and domain adaptation in the e-commerce reviews. The DMP-BTL-DA model improved the sentiment accuracy for the consumer sentiment patterns. The finding can offer valuable data support for the theoretical research and practical applications in agricultural e-commerce.