## 2 Methods  
### Morphological Cognitive Intent Prediction Model  
This study primarily aims to quantify and predict the strength of user cognition of design intentions in product form characteristics, thereby gaining deeper insights into the relationship between product form features and user behavior. This supports product design evaluation, decision-making, and optimization. The process involves five stages: collection of representative samples, selection of typical samples, classification and decomposition of morphological features and factors, calculation of weighted scores for feature factors, and construction and testing of the prediction model, as shown in Figure 1.  
1. Collection of representative samples: Representative samples are essential as they reflect the overall market performance of a product, grounded in user cognition. During the collection process, it is crucial to consider the quantity, diversity, and coverage of the samples to ensure they comprehensively reflect the overall morphological characteristics and variations of the target. The set of representative samples can be denoted as \(S=\{S_{1},S_{2},\cdots,S_{n}\}\), where \(S_{i}\) is the \(i\)-th sample from the set, and \(n\) represents the number of samples.
2. Typical sample selection: Cluster analysis is performed on the representative samples to identify those that are most typical and representative. This facilitates the classification of morphological features and the extraction of characteristic factors.
3. Classification and decomposition of morphological features and factors: Expert evaluation is used to analyze the morphological features of typical samples. The features are classified into categories such as common characteristics, functional intentions, attention, and mass. A set of feature categories, denoted by \(Q\), is established, where \(Q=\{q_{1},q_{2},\ldots q_{n}\}\). Subsequently, the morphological decomposition method is applied to disassemble the categories of morphological features into individual feature factors. These factors are then extracted and encoded to form a set of feature factors, denoted as \(F\). For each feature category \(q_{j}\), there is an associated set of feature  
Figure 1: Morphological feature intention recognition and prediction model.  
factors denoted as \(F_{qj}\). Let \(F_{qj}=\left\{f_{j1},f_{j2},\ldots,f_{jl}\right\}\) represent the entire set, where \(f_{jl}\) is the \(j\)-th feature factor in the \(l\)-th feature category.
4. Calculation of weights and composite intention scores: Weights for morphological feature categories are computed, intentions of feature factors are quantified, and composite intention scores for both morphological feature categories and samples are determined.
5. Construction and testing of the predictive model: Morphological feature category intention scores are used as inputs, while composite intention scores of samples are set as outputs. A morphological intention prediction model is established, and its accuracy is validated using leave-one-out cross-validation.
6. Finally, the feasibility and effectiveness of the method are demonstrated and validated through a practical design case study.