Research on the application of AIGC Technology in E-commerce Platforms Advertising

Abstract

On the basis of the current status of generative artificial intelligence-generated content (AIGC) technology in the field of e-commerce advertising, this study adopts a mixed research methodology, combining sentiment analysis and topic modelling techniques, to systematically explore consumer attitudes, the path of technological practice, and the challenges of industry development. By crawling 679 user comments on the Weibo platform, the study revealed that (1) 56% of the comments presented negative sentiment tendencies, which mainly stemmed from concerns about the credibility of the technology (e.g., the authenticity of the generated content) and ethical risks; (2) thematic analysis revealed that users' concerns were focused on the “efficacy of AI technology” (e.g., the application of large models); and (2) thematic analysis revealed that users' concerns focused on the dimensions of “AI technology effectiveness” (e.g., big model application), “content generation form” (e.g., video advertisement creativity), and “tool suitability” (e.g., DeepSeek tool). The study further combines case studies to validate the practical value of AIGC in content production efficiency (e.g., a 4.2-fold improvement in ad material generation time efficiency) and user experience optimization (e.g., a 19.8% improvement in the conversion rate of virtual try-on scenes). Moreover, this paper reveals the four-dimensional challenges faced by AIGC technology-legal risk (e.g., copyright disputes), ethical bias (e.g., training data bias), quality control (e.g., logical flaws in generated content) and operational thresholds (e.g., differences in engineering capabilities) and advances the construction of multisource data training, standardized prompt frameworks and other coping strategies. This study provides a theoretical basis and practical paradigm for the deep integration of AIGC technology and e-commerce advertising, with both methodological innovation and industrial guidance value.