Impact of Channel Integration on Brand Equity Empowered Based on Deep Learning and Graph Neural Network Approaches

Abstract

This study employs a Graph Neural Network (GNN) to identify predictive associations between channel integration structures and brand equity, with a particular focus on heterogeneous nodes and multi-relation edges in multichannel commercial data. A Multi-Relation Integrated Graph Neural Network (MRI-GNN) is developed by incorporating node feature fitting, relation weight learning, and ensemble message passing into a unified optimization process. In the generalization evaluation, MRI-GNN achieves an accuracy of 93.54% and a recall of 92.39% for node classification on the Digital Bibliography & Library Project (DBLP) dataset, and an accuracy of 90.87% and a recall of 90.84% on the Association for Computing Machinery Citation Network dataset. When only 10% of the DBLP training data is used, the model still achieves an accuracy of 87.68%. In the brand equity prediction task based on multi-source commercial data, MRI-GNN achieves a root mean square error of 15.23, a mean absolute error of 12.11, and a coefficient of determination (R 2 ) of 0.81. Ablation results indicate that node feature fitting, information consistency, and price coordination make relatively substantial contributions to prediction performance. The results demonstrate that MRI-GNN can jointly represent node attributes and multi-relation structures, providing data-driven support for analyzing the association between channel integration and brand equity.