The centralized seed packaging system faces significant challenges in dynamic decision-making due to stochastic demand fluctuations and operational constraints. This paper presents a novel hybrid approach integrating deep reinforcement learning (DRL) with multi-stage stochastic programming (MSP) to optimize decision-making processes. Our method leverages DRL for adaptive learning and MSP for uncertainty modeling, enabling real-time adjustments. Results from case studies demonstrate improved efficiency, reduced costs, and enhanced robustness compared to traditional approaches.