Student Affairs Workflow Optimization Using Proximal Policy Optimization Algorithm

University management systems are increasingly complex, with traditional process management systems facing challenges such as low efficiency, slow response times, and congestion in task scheduling, approval paths, and resource allocation. These limitations hinder the ability of current systems to meet the demands for intelligent and adaptive process optimization in modern educational management. To address these challenges, this paper proposes an intelligent process optimization framework based on the Proximal Policy Optimization (PPO) algorithm. By modeling student affairs management as a Markov Decision Process (MDP), the proposed method dynamically optimizes task allocation and process node selection using reinforcement learning. A simulation environment is built to evaluate the performance of the proposed model, with a focus on metrics such as average task completion time, resource utilization, and system throughput. Experimental results demonstrate that the PPO-based model outperforms traditional rule-based systems and classic deep reinforcement learning models, offering better stability and adaptability in dynamic process environments. This paper contributes to the expanding application of reinforcement learning in educational process management, providing both theoretical and practical insights for the intelligent transformation of university affairs systems.