Abstract: Blockchain suffers from low throughput and poor scalability, thus making it difficult to achieve large-scale practical applications. Although sharding technology can improve performance through parallel processing, traditional sharding schemes have limitations including insufficient reconfiguration flexibility and a single-dimensional node evaluation metric, and cannot balance system throughput and security. To this end, this paper introduced a multi-dimensional node evaluation and intelligent dynamic reconfiguration scheme for sharded blockchain nodes based on deep reinforcement learning (DRL), aiming to balance throughput performance and system security during sharding reconfiguration. Specifically, it designed a multi-dimensional evaluation method to construct more comprehensive dynamic profiles for nodes and quantitatively analyze shard throughput and security. Meanwhile, the DRL algorithm was introduced to identify optimal sharding strategies responsive to network conditions based on system states. Simultaneously, node allocation was dynamically balanced, ultimately achieving intelligent and adaptive adjustment of sharding reconfiguration. The experimental results show that compared with the traditional scheme, the proposed method can effectively improve throughput and reduce sharding risk. Additionally, it achieves a long-term dynamic balance between throughput and security, providing a feasible solution for the application of blockchain in large-scale scenarios.

Key words: sharded blockchain, deep reinforcement learning, reconfiguration, node assignment, scalability