## 7 Conclusions and Future Works  
The assessment and measurement of interface satisfaction have long served as crucial metrics for software optimization and upgrades. Presently, satisfaction measurement methods predominantly rely on subjective evaluations, sometimes supplemented with objective methods. However, subjective approaches often lack objectivity, while objective methods can face efficiency issues. In this study, we collected objective data, including facial expression intensity, task completion time, task completion status, and user operation processes, through facial expression recognition technology. Furthermore, we optimized the BP neural network model using the particle swarm optimization (PSO) algorithm. Subsequently, we constructed a user satisfaction prediction model based on the PSO-BP neural network, which we validated using two established note-taking software platforms. Our results indicate that the model exhibits a high level of accuracy and reliability, with an \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Task 1** & **Task 2** & **Task 3** & **Task 4** & **Average** \\ \hline MAPE & 19.81\% & 12.55\% & 9.13\% & 13.47\% & 13.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction error of customer satisfaction. average absolute percentage error of 13.74% in predicting satisfaction across four typical functional tasks in Goodnotes, and the MAPE of the BP neural network model is 31.79%. Therefore, the user satisfaction model based on the PSO-BP neural network demonstrates relatively high accuracy and good stability in this field, making it an effective tool for predicting user satisfaction. In summary, the user interface satisfaction measurement method proposed here integrates facial recognition technology and the PSO-BP neural network, offering both objectivity and high accuracy. Moreover, it surpasses other objective measurement methods, such as EEG and eye movement tracking, in terms of efficiency. This method can be readily applied to general software satisfaction assessments, providing valuable data and guidance for software interface design, optimization, and upgrades. By expanding the scope of software interface user satisfaction evaluation methods, our study contributes to the advancement of usability research in this domain. There are still shortcomings in this research. This study primarily uses note-taking software as a case study, and whether the conclusions can be applied to other types of software remains to be further analyzed and discussed. Future research will consider including a wider variety of software types as experimental materials to conduct a more extensive analysis of user satisfaction. Secondly, the participants are mainly from first- and second-tier cities with higher education levels, limiting the applicability of the results to other demographic groups. Future studies should include a more diverse sample, considering factors such as gender, occupation, culture, and education, to better understand their impact on user satisfaction. Finally, this study did not explore the possible antagonistic attacks on facial expressions, and subsequent studies will explore these potential factors in depth to enhance the credibility and applicability of the model. Moving forward, our research will continue to delve into user satisfaction and usability, exploring their influencing factors. We aim to incorporate deep learning algorithms to further enhance the model's predictive capabilities, ultimately establishing a satisfaction prediction model with broader applicability and higher accuracy. Conceptualization, P.H., Q.L. and Y.L. ; methodology, Q.L., P.H. and T.W. ; software, Q.L. and P.H. ; validation, P.H., Q.L. and Y.L. ; formal analysis, P.H., B.Z. and Q.L. ; investigation, B.Z., P.H. and T.W. ; data curation, Q.L. and B.Z. ; writing--original draft, P.H. and Q.L. ; writing--review and editing, B.Z., T.W. and Y.L. ; visualization, Q.L. and B.Z. ; supervision, P.H. ; project administration, P.H.