A Holographic Blockchain-Enabled Social Group Consensus Method Using Graph Convolutional Networks With Optimization-Based Sentiment Analysis for IoT Consumer Electronics

The integration of holographic counterpart technologies (HCT) and Internet of Things (IoT) has transformed consumer electronics (CE). In this context, consumers typically choose among multiple options with diverse preferences when purchasing CE products, easily influenced by others. Thus, this scenario is modeled as a social group consensus problem. HCT enhances the decision transparency by converting group decision modules into interactive 3D visualizations. However, product selections often face challenges involving security, interpretability, and accuracy. Therefore, a holographic blockchain-enabled consensus method with optimized sentiment analysis (SA) is explored. First, the bidirectional gated recurrent unit (BiGRU) model’s hyperparameters are optimized via improved secretary bird optimization algorithm (ISBOA) for SA and agreeableness assessments. Second, a convex clustering analysis incorporating trust relationships is constructed. Meanwhile, graph convolutional networks (GCNs) are used to calculate key indicator weights. Third, a blockchain adjustment mechanism based on trust relationships is designed in the first stage of consensus reaching process (CRP), while a minimum-cost consensus model based on agreeableness is incorporated in the second stage. Fourth, Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form (MULTIMOORA) and regret theory (RT) are fused to obtain stable optimal selections. Finally, an example of purchasing solar emergency lights validates the method’s applicability.