基于AIGC的电商内容生成系统效能评估研究

A Study on the Performance Evaluation of an AIGC-Based E-Commerce Content Generation System

摘要: 随着人工智能生成内容(AIGC)技术的迅猛发展，其在电子商务领域的应用正深刻改变着传统商品内容创作模式。本研究旨在构建一个综合性的评估框架，对基于AIGC的电商内容生成系统的效能进行多维度、主客观相结合的评估。研究首先通过文献综述，确立了以内容质量、生成效率、商业转化及人机协同为核心的评估维度。随后，我们设计并实施了一项混合方法研究：在定量分析层面，通过控制实验，对比了AIGC系统与传统人工方式在文案生成速度、多样性、SEO友好度等指标上的差异，并利用A/B测试评估了不同内容对用户点击率(CTR)、转化率(CVR)等关键业务指标的影响；在定性分析层面，通过对电商运营、内容编辑等专业人员的深度访谈，探究了AIGC系统在实用性、易用性、创造性及伦理风险等方面的深层价值与挑战。研究结果表明，AIGC系统在生成效率和规模性上具有压倒性优势(效率提升超80%)，并能有效提升中长尾商品的流量指标(CTR平均提升约15%)；然而，其在内容深度、品牌调性一致性和情感共鸣等方面仍存在不足，存在“算法偏见”和“内容同质化”的风险。最终，本研究提出了一个平衡技术能力与人文价值的AIGC电商内容生成系统优化路径与治理框架，为电商企业及相关开发者提供理论与实践参考。

Abstract: With the rapid advancement of AI-generated content (AIGC) technology, its application in e-commerce is profoundly transforming traditional product content creation models. This study aims to construct a comprehensive evaluation framework for assessing the efficacy of AIGC-based e-commerce content generation systems through multidimensional analysis combining objective metrics with subjective evaluations. The research first establishes core evaluation dimensions—content quality, generation efficiency, commercial conversion, and human-machine collaboration—via a literature review. Subsequently, we designed and implemented a mixed-methods study: At the quantitative level, controlled experiments compared AIGC systems with traditional manual methods across metrics including copy generation speed, diversity, and SEO friendliness. A/B testing was employed to evaluate the impact of different content types on key business indicators such as click-through rate (CTR) and conversion rate (CVR). At the qualitative level, we conducted in-depth interviews with professionals including e-commerce operations specialists and content editors to explore the deeper value and challenges of AIGC systems regarding practicality, usability, creativity, and ethical risks. Findings indicate that AIGC systems possess overwhelming advantages in generation efficiency and scalability (over 80% efficiency improvement) and can effectively boost traffic metrics for mid-to-long-tail products (average CTR increase of approximately 15%); However, they still fall short in content depth, brand tone consistency, and emotional resonance, carrying risks of “algorithmic bias” and “content homogenization”. Ultimately, this study proposes an optimization pathway and governance framework for AIGC e-commerce content generation systems that balances technological capability with humanistic value, providing theoretical and practical references for e-commerce enterprises and relevant developers.