The rapid growth of large language models has increased the need for high-quality data, making efficient data acquisition a challenge. Blockchain-based crowdsourcing enhances data annotation efficiency in untrusted environments, but existing static or heuristic matching methods struggle in high-dimensional dynamic settings. This paper proposes an intelligent crowdsourcing framework using deep reinforcement learning for blockchain scenarios to improve data annotation in complex dynamic environments. The framework employs deep reinforcement learning for intelligent task-worker matching and dynamic allocation, optimizing scheduling based on task difficulty, worker performance, and resources. Blockchain smart contracts ensure secure, traceable, and fair user management, task management, and auditing. Experiments show the framework outperforms static and heuristic methods, improving on-time completion rate and transaction volume by 7.7% and 15% over heuristic strategies, and 6.5% and 14.3% over static strategies in high-dimensional dynamic environments.