## 2 Methods
### Calculation of Morphological Feature Category Weights  
The entropy weighting method is used to calculate the weights (\(W\)) of feature categories. Entropy, a concept from information theory, quantifies the uncertainty and orderliness of information [40]. During the evaluation of a given index, entropy objectively measures uncertainty: the more disordered, dispersed, and random the data, the higher the uncertainty and entropy, and thus, a smaller weight is assigned; conversely, the more ordered the data, a larger the weight given. The entropy weighting method constructs weights for morphological feature categories based on objective data, eliminating subjectivity in the evaluation process. Quantification of cognitive intentions for morphological feature category \(Q\) is performed across six dimensions: functional orientation, behavioral intention, recognizability, cognitive load, attention allocation, and experiential feeling. This quantification utilizes a 9-point scale. Details of the discriminative indices for morphological feature categories and factors are provided in Table 1, and the measurement criteria are listed in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Discrimination Indices** & **Explanation of Indicators** \\ \hline Functional orientation & Assessment of participants’ acceptance and understanding of functional information conveyed by the product form. \\ \hline Behavioral intention & Evaluation of participants’ ability to correctly use or operate the product and their willingness to use it after initial exposure. \\ \hline Recognition accuracy & Assessment of participants’ ability to recognize the design intentions of different product forms. \\ \hline Cognitive load & Evaluation of cognitive load in participants when understanding and recognizing the design intentions of different product forms. \\ \hline Attention allocation & Assessment of attention allocation among participants when exposed to different product forms, including their level of focus and attraction to various design elements. \\ \hline User experience & Evaluation of participants’ overall user experience with different product forms, assessing aspects such as usability, comfort, and pleasure. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Discrimination indices for cognitive intentions of morphological feature categories and factors. First, utilize Formulas (3) and (4) to preprocess the data through positive and negative indicator standardization. Negative indicators are better when lower, thus requiring negative indicator standardization. Secondly, compute the probability and entropy values using Formulas (5) and (6). Then, apply Formulas (7) and (8) to obtain the coefficient of variation and the weight \(W_{i}\) of the feature categories (the weight of the \(i\)-th morphological feature category). Larger entropy values and smaller coefficients of variation indicate greater orderliness of the indicators. Conversely, larger coefficients of variation indicate greater information content and consequently greater weights for the corresponding indicators. Normalization of positive indicators:  
An increase in a positive indicator typically represents an enhancement or improvement in a certain characteristic. The characteristic of positive indicators is that the higher the value, the better the evaluation result, such as in the case of user satisfaction. For positive indicators, the standardization method typically involves converting the raw data into a value between 0 and 1, facilitating subsequent comprehensive analysis and evaluation.