This study focuses on user experience from the perspective of big data to complete the aggregation, clustering, and visual presentation of knowledge. Using a combination of sample literature review, visualization technologies, knowledge map analysis, Carrot2 clustering, and other methodologies, this study intends to examine user experience from three perspectives: research state, hotspots, and trends. First, based on the double-map overlay, core institutions, core countries, core authors, core journals, and core references distribution research, the knowledge flow, research power, and research subjects of user experience are analyzed. Secondly, through keyword clustering analysis, this research intuitively presents the research topics of user experience and reveals the research hotspots and the evolution path of research methods. Finally, with the help of the subject clustering algorithm, the emerging trends of user experience research are predicted: the immersive experience upgrade of multi-scenario integration, the innovative design of multi-role collaboration, and the cross-disciplinary interactive exploration of multi-discipline. Following this, the user experience knowledge map is constructed, providing a global view and macro-cognition for subsequent research.

To enhance the online education service experience, the emotional valence of the user was studied as an evaluation variable, and both qualitative and quantitative research were used to find how to evaluate online education service touchpoints. First, deconstruct the system service interface with the interactive touchpoint matrix, set service evaluation indicators from four aspects, visual guidance, learning resources, after-class evaluation, and interactive feedback, and build an online education service touchpoint evaluation system. Secondly, using Tencent Classroom as the target of research, an online education service rating experiment is created based on the two dimensions of emotional valence and perceptual cognition. With the aid of a questionnaire survey and analytic hierarchy process (AHP), a multidimensional evaluation of online education service touchpoints is accomplished using the learners’ emotional enjoyment, activation, dominance, touchpoint satisfaction, and importance as measuring indicators. Finally, concluding the assessment and optimization of online education service touchpoints, the evaluation data for the service are combined, and the evaluation results are generated using visual design. This study includes successful strategies and practical recommendations for boosting interest in e-learning services, user initiative, and excitement for learning.

With the continuous expansion of urban metro systems, balancing passenger experience and operational efficiency has become a central concern in contemporary public transportation design. However, most existing metro service studies continue to focus on perceptual comfort or isolated usability tasks and lack an integrated, behavior-centered perspective that accounts for the full travel chain and diverse user groups. This study develops the Bi-directional Service Behavioral Experience Model (BSBEM), which systematically integrates one-way navigation behaviors and interactive operational behaviors within a unified dual-path framework to identify behavioral patterns and experiential disparities across user groups. Based on the People–Touchpoints–Environments–Messages–Services–Time–Emotion (POEMSTI) behavioral observation framework, this study employs a mixed-method approach combining video-based behavioral coding, usability testing, and subjective evaluation. An empirical study conducted at Beidajie Station on Xi’an Metro Line 2 involved three representative passenger groups: high-frequency commuters, urban leisure travelers, and special-care passengers. Multi-source data were collected to capture temporal, spatial, and interactional dynamics throughout the travel process. Results show that high-frequency commuters demonstrate the highest operational fluency, urban leisure travelers exhibit greater visual dependency and exploratory pauses, and special-care passengers are most affected by accessibility and feedback latency. Further analysis reveals a positive correlation between route complexity and interaction delay, highlighting discontinuous information feedback as a key experiential bottleneck. By jointly modeling one-way and interactive behaviors and linking group-specific patterns to concrete metro touchpoints, this research extends behavioral evaluation in metro systems and offers a novel behavior-based perspective along with empirical evidence for inclusive, adaptive, and human-centered service design.