## 2 Related Research  
Various methods exist for assessing user satisfaction, with subjective evaluation being the primary technique, supplemented by both subjective and objective comprehensive assessments. Currently, the evaluation of interface satisfaction primarily includes user subjective assessments, objective evaluations of behavioral and physiological data, and assessments based on computer algorithms. The subjective approach to evaluating user satisfaction typically involves collecting subjective and experiential data from users via Delphi methods, questionnaire surveys, interview surveys, and think-aloud protocols [3; 4; 5; 6]. These methods are straightforward and form the cornerstone of user satisfaction assessment. However, an exclusive reliance on subjective evaluation invites concerns regarding accuracy and objectivity. Several studies have employed objective behavioral analysis and physiological data metrics to assess user satisfaction with specific products and software. Utilizing usability testing foundations, objective behavior analysis has emerged as a prevalent method for evaluating software interface satisfaction [7; 8]. Several studies have utilized a combination of online reviews [9], Delphi methods, and interview surveys to measure product and interface satisfaction within specific domains, such as user satisfaction with digital resources [10; 11]. Online reviews are effective for assessing user satisfaction but are limited to software with extensive user feedback. Physiological measures, notably EEG, eye movement analysis, and facial recognition technology, are widely used in evaluating interface satisfaction. [PERSON] et al. [12; 13] demonstrated that EEG technology analysis could be utilized for assessing interface satisfaction, revealing a correlation between satisfaction assessments and neural responses. EEG data are accurate and objective but have a complex and time-consuming experimental process with strict participant criteria, reducing research efficiency. Eye movement experiments are prevalent in evaluating interface satisfaction, with numerous studies establishing a correlation between objective eye movement data and users' subjective satisfaction evaluations [14; 15; 16; 17]. Eye tracking data primarily reflect visual features and aesthetic preferences, not emotions and feelings. Related studies typically employ eye-tracking data to substantiate the usability of software interfaces, subsequently deriving interface satisfaction evaluations from the established link between usability and satisfaction. However, these studies do not primarily focus on interface satisfaction evaluation. Facial expression recognition technology is another tool for user-satisfaction-related research. [PERSON] et al. [18] proposed using facial expressions as indicators in usability research, offering a basis for assessing users' subjective satisfaction. [PERSON] et al. [19] applied questionnaires, scales, and FaceReader 9.1 software to explore how age-related colors affect product satisfaction. With the progression of usability research methods, the measurement and analysis of expressions are garnering increased attention. Acquiring physiological data, like EEG and eye movements, necessitates using specialized, costly equipment such as eye trackers and EEG machines. In contrast, gathering expression data is more straightforward and cost-effective, requiring only a standard camera to capture facial expressions. This approach notably reduces experimental costs and enhances testing efficiency. Facial expressions directly mirror users' emotional states and are closely linked to satisfaction, making facial expression recognition and analysis technologies practical and valuable for evaluating satisfaction. Accordingly, this study employs facial expression recognition technology to assess interface satisfaction. The rapid progression in computer algorithms has led to the widespread adoption of various prediction models.