为应对人地冲突引发的空间争议，多智能体系统（MAS）已被用于模拟利益相关者互动并生成土地利用优化的系统性解决方案。然而，现有MAS常难以刻画土地利用的时空动态特征，且将系统性领域知识整合至基于智能体的决策过程仍具挑战，制约了模型精度与可解释性的提升。本研究通过构建基于机器学习的时空数据驱动领域知识，增强智能体对环境的感知能力，以弥补上述不足；进一步提出MLAS-ACO模型，将多层级智能体系统（MLAS）与蚁群算法耦合，借助仿生策略优化智能体的空间映射与交互决策。武汉案例研究表明：（1）基于CatBoost的适宜性评价准确率高（F1分数 > 0.88），结合空间集聚特征可为优化提供可靠依据；（2）MLAS-ACO整体目标值提升6.84%，景观格局得到改善，且空间布局符合区域发展战略；（3）相较于MLAS，MLAS-ACO收敛速度提高18.7%，整体目标值提升0.35%；（4）情景分析表明，协调型情景可实现多目标协同，凸显建设用地向高适宜性区域集聚的趋势。MLAS-ACO有助于提升空间优化的可解释性与决策适应性，为精细化空间模拟提供方法支撑，并服务于政策情景下的规划决策。

To address spatial disputes from human-land conflicts, multi-agent system (MAS) has emerged for simulating stakeholder interactions and developing systemic solutions in land use optimization. However, existing MAS often fail to capture spatiotemporal land-use dynamics, while integrating systematic domain knowledge into agent-based decision-making remains challenging, constraining improvements in accuracy and interpretability. This study addresses these gaps by constructing a spatiotemporal data-driven domain knowledge using machine learning to enhance agent perception. Furthermore, we construct MLAS-ACO, a model coupling a multi-level agent system (MLAS) with ant colony algorithm, to refine agent mapping and interactive decisions through bio-inspired strategies. The Wuhan case study demonstrates: (1) CatBoost-based suitability evaluation achieves high accuracy (F1 score > 0.88), and integration with spatial agglomeration features provides a reliable basis for optimization. (2) MLAS-ACO achieves 6.84% higher overall objective, improves landscape pattern, and yields a layout consistent with development strategies. (3) Compared to MLAS, MLAS-ACO improves convergence speed by 18.7% and the overall objective by 0.35%. (4) Scenario results indicates coordinated scenario enables multi-objective synergy, underscoring construction land agglomerating in highly suitable zones. MLAS-ACO contributes to enhancing interpretability and decision adaptability of spatial optimization, offering insights for refined spatial simulation and supporting planning under policy scenarios.