Analysis of AI Product Interaction Research Hotspots and Trends Based on Cite Space, taking WOS Data as an Example (2016-2025)

Currently, AI product interaction, as a major part of the human-computer interaction field, has a significant impact on user experience, industry applications, and technological development.The importance of AI product interaction lies in its ability to change the way users communicate with systems and improve service quality.Current research on AI product interaction suffers from several shortcomings, including a lack of clear main themes, fragmentation of hot topics, and a narrow perspective.This study aims to explore the research trends and analyze research hotspots in AI product interaction, contributing to the theoretical construction and application innovation of related fields.To achieve these goals, a quantitative analysis method was adopted, using CiteSpace 6.4 R1 literature visualization analysis software to conduct a visual analysis of AI product interaction literature data from Web of Science (WOS) from 2016 to 2025.The study found that: 1) Keyword clustering analysis revealed that discussions on AI product interaction focused on optimizing interaction methods, improving user experience, and developing intelligent response mechanisms based on large models; 2) Time trend analysis indicated that research topics are shifting from technology-driven to user-demand-driven; 3) Case studies highlighted the importance of interactive capabilities demonstrated by emerging models such as GPT, which have made significant contributions to long-duration dialogues, knowledge retrieval, and task collaboration.Based on this, the study proposes that safety and transparency are crucial principles for the future development of AI product interaction.Governments need to improve regulatory frameworks, companies need to strengthen product responsibility, and researchers need to promote methodological innovation and pay attention to ethical risks.