Cache Optimization in Consortium Blockchain System Based on GCN and XGBoost

With the rapid development of the global carbon market, the application of consortium blockchain technology in carbon trading and carbon neutrality management has become increasingly widespread, ensuring the security and transparency of transaction data. However, as the transaction scale contin-ues to expand, the on-chain storage capacity and query efficiency of block-chain systems have gradually become key factors limiting system perfor-mance. Although traditional off-chain storage solutions have alleviated the pressure on on-chain storage to some extent, high-concurrency access sce-narios still face the challenge of high query latency. While in-memory cach-ing methods can improve query speed, they are often limited by memory ca-pacity, making it difficult to meet the query response time requirements when dealing with large amounts of data. To address these issues, this paper proposes a cache optimization strategy based on transaction access predic-tion, combining Graph Convolutional Networks GCN and Extreme Gra-dient Boosting XGBoost . The method first constructs a relationship graph of transaction data using GCN and extracts the structural features of trans-action nodes. It then combines the XGBoost model to predict access fre-quency and dynamically adjusts the cache replacement strategy. Experi-mental results show that, compared to traditional algorithms, the proposed method significantly improves cache hit rates and optimizes query perfor-mance in high-concurrency trading environments. This study provides an intelligent optimization solution for cache management in blockchain trad-ing systems, which is of great significance for improving the operational ef-ficiency of the carbon trading market.