## 1 Introduction  
With blockchain technology maturing rapidly, consortium blockchains have emerged as a compelling hybrid solution, combining strengths from both public and private blockchain models [1]. They achieve decentralization, transparent data governance, and immutability while offering substantial benefits in privacy preservation, precise access control, and efficient resource sharing. Consequently, consortium blockchains are receiving increased attention across a diverse range of sectors. This widespread adoption is evidenced by impactful real-world applications. For instance, in the realm of humanitarian aid, the United Nations World Food Programme's \"Building Blocks\" project utilizes a consortium blockchain to securely and efficiently distribute cash assistance to refugees, significantly reducing transaction costs and enhancing transparency [2]. Similarly, driven by stringent regulations such as the U.S. Drug SupplyChain Security Act (DSCSA), the pharmaceutical industry has established consortia like MediLedger to ensure the traceability and verification of prescription drugs, combating counterfeiting and improving patient safety. In the energy sector, major players like Shell and BP have backed the VAKT platform, which leverages a consortium blockchain to streamline post-trade processing for physical energy commodities, increasing efficiency and reducing the risk of errors [3]. These examples highlight the technology's growing role in financial institutions, governmental sectors, and academia [4]. At the heart of consortium blockchain functionality are consensus algorithms, which are essential for maintaining data consistency, system reliability, and overall security [5]. The choice and performance of these consensus mechanisms significantly impact the practical effectiveness of consortium blockchains, particularly in domains demanding high reliability, such as financial services, e-government platforms, and supply chain management systems [6]. Efficient and secure consensus processes directly influence operational continuity and user confidence. Practical Byzantine Fault Tolerance (PBFT) has become a widely adopted consensus protocol for consortium blockchains due to its deterministic nature, rapid finality, low computational demands, and energy efficiency. PBFT ensures consensus and data verification through iterative interactions among known network nodes, making it particularly suitable for stable network environments [7]. However, the original PBFT protocol exhibits critical limitations. Firstly, as the network scales, PBFT suffers from exponential growth in message complexity and communication overhead, drastically increasing consensus latency and hindering performance in large-scale deployments. Secondly, PBFT inherently lacks an effective mechanism to dynamically evaluate and manage node credibility, making it vulnerable to malicious or underperforming nodes. This deficiency reduces proactive governance and node incentivization, compromising both performance and system security [8]. Lastly, the conventional PBFT employs a simplistic round-robin mechanism for selecting primary nodes, inadvertently causing primary node centralization risks. Prolonged dominance by a small subset of nodes can introduce security vulnerabilities and limit adaptive network management [9]. Considering these inherent limitations, there is a clear need to enhance the existing PBFT algorithm. Therefore, this study aims to tackle three crucial research questions:  
1. What kind of node reputation evaluation and classification mechanism can effectively and dynamically manage node behaviors, thus boosting overall system performance and security? 2. How can the primary node selection process be refined to mitigate risks of centralization and enhance resilience against Byzantine failures? 3. What methods can practically reduce communication complexity and consensus latency when applying PBFT algorithms in large-scale networks?