In response to the critical need for scientific evaluation and efficient resource allocation in modern university student management, particularly in relation to labor education, this study proposes a novel intelligent management framework based on a Dynamic Ant Colony Division of Labor (DACLD) model. Existing systems often suffer from static resource allocation, summative evaluation methods, and an inability to foster holistic student development. To address these limitations, we developed a swarm-intelligence-driven ecosystem in which diverse student needs are modeled as dynamic “tasks” and administrative resources are represented as adaptive “agents.” The DACLD core enables self-organizing, real-time task–resource matching through stimulus–response and threshold-adaptation mechanisms. We constructed a comprehensive student development evaluation index system aligned with the CIPP model. Simulation results involving 1,000 virtual students demonstrated that the proposed DACLD-based system significantly outperformed traditional algorithms, including ACO, PSO, and FCFS, in terms of task completion rate (95.2%), average processing time (3.1 days), resource utilization (88.5%), and load balancing.
