The purpose of this study is to provide insights into the research progress of Eye Tracking in User Experience Research (ETUER) by bibliometric methods. The literature of ET-UER collected from the Web of Science database is used as the data source. VOSviewer and CiteSpace, which are software tools for visualizing bibliometric data, are used to conduct keyword analyses, evolutionary analyses, and co-citation analyses of the literature. The results show that the overall trend of literature volume is increasing. Country analysis shows that a few academically strong countries have contributed most of the research in this field. Analysis of research institutions and authors shows that international cooperation in eye tracking user experience research is not close. The hotspots of ET-UER include three main clusters: #1, the variables and evaluation content of eye tracking research; #2, the application scenarios of eye tracking; and #3, the indicators of eye tracking research. Evolution analysis reveals four trends in the development of ET-UER: firstly, the expansion of research scenarios; secondly, changes in eye tracking experimental environments and stimulus materials; thirdly, advances in research methods, paradigms, and analytical technologies; and fourthly, user-centered design. The most frequently co-cited research on ET-UER is divided into three categories: application of eye tracking technology, research on eye tracking technology, and methods and measurement. Based on the analysis of this study, the following three questions are still worth further attention. First, it is necessary to optimize the user experience of eye tracking as an interactive input. Second, it is important to support continuous and reliable research on gaze behavior in real-world experimental environments, including dynamic, 3D, interactive, and other experimental materials, which require more advanced experimental and data analysis techniques. Third, machine learning has great potential for follow-up research in the field of ET-UER.