Comparative Opinion Quintuple Extraction (COQE) is a task of recognizing comparative relationships in a sentence-level comment. It is additionally required to extract the quintuple constituents that attribute to a specific comparative relation, including a subject and object, as well as comparative aspect, opinion and preference. Previous study employ pipeline models in addressing the COQE task, which tend to suffer from error propagation. To address the issue, in this paper, we propose a BERT-based end-to-end neural model as the alternative. Specifically, we first boil COQE down to a set prediction problem, due to the finding that all the quintuple constituents fail to hold coherent or sequential relationships. In other word, we consider a group of quintuple components as a set, and intent to bag-and-drag them as a whole, instead individually and one-by-one. Furthermore, we leverage Graph Convolutional Network (GCN) to enhance the end-to-end model, which plays the role of perceiving and representing the relevant relations among the quintuple components. We experiment on three benchmark datasets, including Camera-COQE, Car-COQE and Ele-COQE. The experimental results show that our model (GCN-E2E) yields a significant improvement in most cases, compared to the BERT-based pipeline baseline. The performance reaches the F1-scores of 14.10%, 36.46% and 39.29%, with the improvements of 0.74%, 6.71% and 8.56%.