Blockchain-Based Multimodal Semantic Sharding System for Digital Copyright Utilizing Graph Clustering

Abstract

The rapid evolution of internet technologies has triggered exponential growth in multimodal data, intensifying security and efficiency challenges in digital copyright protection. This paper proposes a Multimodal Semantic Sharding Graph Convolutional Blockchain System (MSSGC) that innovatively integrates multimodal semantic fragmentation with graph convolutional neural networks (GCN). The system enables load-balanced blockchain dynamic sharding through GCN-based joint clustering of semantic features and transaction networks, effectively reducing cross-shard communication overhead. We develop a Trust-enhanced Proof of Stake (T-PoS) protocol to optimize account sharding via incentive mechanisms while maintaining decentralization and network equilibrium, complemented by queuing theory-based analysis of transactional performance ceilings. Experiments on blockchain simulators demonstrate that MSSGC significantly outperforms baseline systems across diverse sharding configurations: throughput improves by approximately$\mathbf{2 0 \%}$, latency is reduced by$\mathbf{3 0 \%}$, and load-balancing efficiency increases by roughly 2.3. Notably, the system maintains about 70% transaction integrity even under malicious attacks. This work bridges theoretical gaps in semantic-aware copyright protection while advancing practical blockchain implementations for digital rights management.