## 2 Methods
### Typical Sample Selection  
A morphological similarity discrimination matrix is constructed by comparing the similarity of morphological features between pairs of samples, which is then mapped to distances. Classification is achieved by evaluating these distances between samples. When conducting morphological similarity discrimination for representative samples, the participants should include individuals with industry design experience and those from the target demographic with relevant experience. This approach facilitates a deeper understanding of morphological features and integrates cognitive differences between the two groups effectively. Multidimensional scaling (MDS) is applied to analyze the samples, serving as a data dimensionality reduction method for examining the similarity or dissimilarity between samples. MDS transforms the similarity or dissimilarity between samples into spatial distances or positional relationships, allowing high-dimensional data to be mapped onto a lower-dimensional space [39]. This facilitates a more intuitive understanding and visualization of the relationships between samples, particularly their similarities and differences. It helps researchers visualize data in two-dimensional or three-dimensional space. In the low-dimensional space after MDS dimensionality reduction, each point represents an object; the smaller the distance between points, the more similar the objects are. The basic process of MDS analysis is as follows: First, construct a morphological similarity matrix to evaluate the similarity between samples. Calculate the Cronbach's alpha coefficient to assess reliability; an alpha \(\geq\) 0.8 indicates good reliability. Next, determine the number of dimensions and plot a scree plot, which visually shows the dimensions and their contributions in the data. Additionally, calculate the stress index and the RSQ (R-squared) value for the selected dimensions using Equations (1) and (2), respectively. The stress index is used to assess how well the dimensionality-reduced data retain the structure of the original high-dimensional space. A smaller stress value indicates a smaller difference between the distances in the low-dimensional space and the original high-dimensional space, suggesting a better performance of the MDS model. A stress value below 0.1 indicates an excellent fit; between 0.1 and 0.2, an acceptable fit; and above 0.2, a poor fit. The RSQ value is used to evaluate the model's ability to preserve the original data structure in the low-dimensional space. It represents the similarity or consistency between the original high-dimensional distance matrix and the low-dimensional distance matrix. The closer the RSQ value is to 1, the more the model retains the original data structure in the low-dimensional space. Typically, an RSQ value above 0.9 represents an excellent fit; above 0.8, a good fit; and below 0.8, a poor fit. The formula for calculating the stress index is  
\[\centering Stress=\sqrt{\frac{\sum_{i<j}\left(s_{ij}-s_{ij}\right)^{2}}{\sum_{i< j}s_{ij}^{2}}}\@add@centering \tag{1}\]  
where \(s_{ij}\) is the distance between samples \(i\) and \(j\) in the original high-dimensional space;\(\hat{s}_{ij}\) is the distance between samples \(i\) and \(j\) in the low-dimensional space. The formula for calculating the RSQ is  
\[RSQ=1-\frac{\sum_{i<j}{(s_{ij}-\hat{s}_{ij})^{2}}}{\sum_{i<j}{(s_{ij}-\overline{ s})^{2}}} \tag{2}\]  
where \(s_{ij}\) is the distance between samples \(i\) and \(j\) in the original high-dimensional space;  
\(\hat{s}_{ij}\) is the distance between samples \(i\) and \(j\) in the low-dimensional space;  
\(\overline{s}\) is the mean of all original high-dimensional distances. The dimensional coordinates representing the samples obtained through MDS are subjected to systematic cluster analysis. This study employs the between-group linkage clustering method, combined with Pearson correlation for interval measurement. Z-scores are used for standardization, a dendrogram is plotted, and the coordinates of the cluster centroids are calculated.