As reliable distributed systems, Blockchains have been widely applied in diverse domains, such as the Internet of Things (IoT). Recent studies have explored Deep Reinforcement Learning (DRL) to enhance blockchain performance. However, existing DRL-based blockchain performance optimization methods rely on implicit and idealized assumptions about node behaviors and transactional workloads, limiting their effectiveness and efficiency on the blockchain with dynamic work-loads and heterogeneous nodes. To alleviate this, we propose CHAINOPT, a novel blockchain performance optimization framework devised for optimal parameter configuration to handle dynamic workloads and heterogeneous nodes. Specifically, we first propose an interaction-aware state representation learning module to model both global system-level and local heterogeneous node feature interactions to generate better state representations. Then, a contrastive learning-enhanced workload identification module is designed to extract discriminative workload-specific state representations to improve workload identification accuracy. Finally, we design a workload-similarity guided policy reuse module to produce effective reuse weights to transfer knowledge from history policies based on workload relevance, thereby improving optimization speed and stability. Extensive experiments show the effectiveness of CHAINOPT in improving blockchain performance, achieving 185.87% higher scalability and 1492.16% stronger security with only a marginal 6.26% latency increase. Moreover, it outperforms baselines in static scenarios while maintaining considerable superiority under varying workloads.