## 3 Research Process and Methods
### Constructing the AHP Judgment Matrix  
The main role of the hierarchical analysis method (AHP) in this study is to systematically evaluate user needs in the design of food ordering interfaces. AHP divides the need hierarchy into goal, criterion, and indicator layers by constructing a judgment matrix, thus ensuring scientific, systematic, and structured design decisions. In addition, AHP provides support for the application of KE, which can effectively integrate users' emotional needs into the design optimization process, bringing the interface design more in line with users' actual needs. By sorting out and summarizing the user requirements of the ordering interface design, the requirement hierarchy is divided into three layers: the goal layer, the criterion layer, and the indicator layer. The first layer (goal layer) is defined as the perceived semantics of the ordering interface A. This represents the ultimate goal of this study, i.e., the interface design's comprehensive satisfaction of users' emotional and functional needs. The second layer (criterion layer) consists of three aspects: \"Operation and Functionality B\({}_{1}\)\", i.e., focusing on the simplicity of the interface's operation and the completeness of its functionality; \"Visual and Emotional Impact B\({}_{2}\)\" evaluates the visual appeal of the interface and its emotional impact on the user. \"Interaction and Feedback Experience B\({}_{3}\)\" assesses the smoothness of user interaction with the interface and the timeliness of feedback. The third layer (indicator layer) is defined as the eight affective imagery words extracted from the above principal-component-based analysis, which are used as specific indicators for assessing the criterion layer. The final hierarchical model of the ordering interface is shown in Figure 6.  
Based on the user requirement classification system of the perceptual semantics of the ordering interface, a multi-objective decision-making model is constructed using the AHP hierarchical analysis method to quantify the requirements weights [61]. In the decision \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Factor Loading Coefficient**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Fracipal Component 1**} & \multicolumn{2}{c}{**Principal Component 2**} & \multicolumn{2}{c}{**Principal Component 3**} \\ \hline Complex–Simple & \(0.612\) & & \\ Uncomfortable–Comfortable & \(-0.678\) & & \\ Unpleasant–pleasant & & & \(-0.545\) \\ Obscure–intuitive & \(-0.501\) & & \\ Outdated–Modern & & \(0.654\) & \\ False–Trusted & & & \(0.647\) \\ One–way–interactive & & & \\ Indifferent–friendly & & \(-0.727\) & \\ Distracted–Focused & \(0.693\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Table of mean scores for the perceptual semantic evaluation of sample ordering interfaces. Figure 6: AHP hierarchical model of perceived semantics in the design of the ordering interface. Source: own processing. making process, we invited 12 experts with design backgrounds to form an expert group, including two senior design directors (with more than 10 years of practical experience in the industry, familiarity with the solutions to actual design projects, and an ability to provide valuable advice on the user experience, technical feasibility, and commercialization of interfaces), and three university professors (with rich theoretical backgrounds and project guidance experience, especially in the field of interface design, perception analysis and user behavior); they were able to provide innovative and forward-looking design insights. The team also included seven design graduate students. We conducted a pairwise comparison of the elements of the criteria layer and the indicator layer and standardized the criteria according to the Saaty 1-9 scaling method construct [62]. The judgment matrix was constructed according to the principle of antisymmetry, i.e., for each pair of elements A and B, if the importance of A with respect to B is x, then the importance of B with respect to A is 1/x.