The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.

翻译：人工智能驱动的搜索引擎的普及已将信息发现从传统的基于链接的检索转变为直接生成答案并选择性引用来源，这对内容可见性带来了新的挑战。尽管现有的生成式引擎优化方法主要关注语义内容修改，但结构特征对引用行为的影响仍未被充分探索。本文提出GEO-SFE，一个面向生成式引擎优化的系统性结构化特征工程框架。该方法将内容结构分解为三个层次：宏观结构（文档架构）、中观结构（信息分块）和微观结构（视觉强调），并建模它们对不同生成式引擎架构中引用概率的影响。我们开发了架构感知的优化策略与预测模型，能够在保持语义完整性的同时提升结构有效性。在六个主流生成式引擎上的实验评估显示，引用率（提升17.3%）和主观质量（提升18.5%）均获得一致性改进，验证了所提框架的有效性与泛化能力。本研究将结构化优化确立为生成式引擎优化的基础组成部分，为在大语言模型驱动的信息生态系统中提升内容可见性提供了数据驱动的方法论。