Objective: Generative artificial intelligence (GAI), exemplified by models such as ChatGPT, is driving disruptive technological transformations. Its rapid and widespread adoption presents dual challenges: job displacement and skill renewal, placing unprecedented pressure on the public's sense of occupational security. To clarify the mechanisms through which GAI adoption affects occupational security, this study analyzes public commentary data from major social media platforms. Sentiment analysis and topic modeling are employed to identify key influencing factors, map their causal relationships, and construct a hierarchical structure. The aim is to offer targeted mitigation strategies to address the occupational security challenges arising from GAI adoption. Methods: Public comments related to the occupational impact of GAI were primarily collected from TikTok, with additional data obtained from Weibo and bilibili, all of which are widely used social media platforms in China. After data cleaning and manual filtering, a bidirectional encoder representations from Transformers—based sentiment classification model was employed to extract comments expressing negative sentiment, resulting in a perception-based corpus focused on occupational insecurity triggered by GAI adoption. The biterm topic model was then used for topic modeling, identifying eight core themes—including employment and societal dynamics and human-AI collaboration. Semantic analysis of topic keywords distilled seven critical influencing factors. The decision-making trial and evaluation laboratory (DEMATEL) method was applied to construct an influence matrix quantifying the strength and direction of causal relationships among these factors. Finally, interpretive structural modeling (ISM) was used to build a hierarchical structure of the influencing factors, revealing their stratified distribution and transmission pathways. Results: The impact of GAI adoption on public occupational security was found to result from interconnected, multi-level structural factors. Based on DEMATEL analysis, the seven influencing factors were categorized into four functional zones: core driving, auxiliary support, adaptive adjustment, and comprehensive transmission. Human-AI collaboration was placed in the core driving zone, exerting a strong influence on other factors. Employment market changes, situated in the comprehensive transmission zone, showed the highest centrality. This factor was significantly influenced by other variables, indicating its pivotal role in the overall impact mechanism. ISM further revealed a three-level hierarchical structure. The foundational level included human-AI collaboration and legal/ethical and privacy safeguards, which initiated the occupational security shock. The middle level comprised factors such as future development prospects, industrial restructuring, and infrastructure development, which reflected structural adjustments driven by technological change and functioned as transitional nodes. The top level encompassed digital literacy education and employment market changes, representing the most direct pathways through which GAI impacted the public's occupational security. Conclusions: Based on these findings, this study proposes a multi-level response framework from the perspectives of individuals, enterprises, social organizations, and government actors to address the occupational security challenges posed by GAI adoption. Furthermore, the analytical framework developed herein provides a theoretical foundation and practical reference for future research on occupational risk assessment and governance strategies in the era of rapid GAI advancement, thereby supporting the coordinated development of technological progress and societal stability.