This paper proposes DeepRule, an integrated framework for automated business rule generation in retail assortment and pricing optimization. Addressing the systematic misalignment between existing theoretical models and real-world economic complexities, we identify three critical gaps: (1) data modality mismatch where unstructured textual sources (e.g. negotiation records, approval documents) impede accurate customer profiling; (2) dynamic feature entanglement challenges in modeling nonlinear price elasticity and time-varying attributes; (3) operational infeasibility caused by multi-tier business constraints. Our framework introduces a tri-level architecture for above challenges. We design a hybrid knowledge fusion engine employing large language models (LLMs) for deep semantic parsing of unstructured text, transforming distributor agreements and sales assessments into structured features while integrating managerial expertise. Then a game-theoretic constrained optimization mechanism is employed to dynamically reconcile supply chain interests through bilateral utility functions, encoding manufacturer-distributor profit redistribution as endogenous objectives under hierarchical constraints. Finally an interpretable decision distillation interface leveraging LLM-guided symbolic regression to find and optimize pricing strategies and auditable business rules embeds economic priors (e.g. non-negative elasticity) as hard constraints during mathematical expression search. We validate the framework in real retail environments achieving higher profits versus systematic B2C baselines while ensuring operational feasibility. This establishes a close-loop pipeline unifying unstructured knowledge injection, multi-agent optimization, and interpretable strategy synthesis for real economic intelligence.

翻译：本文提出DeepRule，一种用于零售品类与定价优化的自动化业务规则生成集成框架。针对现有理论模型与现实经济复杂性之间的系统性错位，我们识别出三个关键缺口：（1）数据模态不匹配，其中非结构化文本源（如谈判记录、审批文件）阻碍了准确的客户画像构建；（2）动态特征纠缠挑战，体现在非线性价格弹性与时变属性的建模中；（3）由多层业务约束导致的操作不可行性。我们的框架针对上述挑战提出三层架构。我们设计了一种混合知识融合引擎，利用大语言模型（LLMs）对非结构化文本进行深度语义解析，将分销协议与销售评估转化为结构化特征，同时整合管理专业知识。随后采用博弈论约束优化机制，通过双边效用函数动态协调供应链利益，将制造商-分销商利润再分配编码为层级约束下的内生目标。最后，通过可解释的决策蒸馏接口，利用LLM引导的符号回归来发现并优化定价策略及可审计的业务规则，将经济先验（如非负弹性）作为数学表达式搜索过程中的硬约束嵌入。我们在真实零售环境中验证了该框架，相比系统性B2C基线实现了更高的利润，同时确保了操作可行性。这建立了一个闭环流程，统一了非结构化知识注入、多智能体优化与可解释策略合成，以实现真实经济智能。