## 1 Introduction
[PERSON] et al. developed a personal information fuzzy emotional inference model (BDFEI) based on deep fusion networks for understanding emotional intent and recognizing emotional behavior in human-computer interaction [28]. [PERSON] et al. conducted a survey study in a Filipino community senior center and constructed a theoretical model of how behavior intent influences elderly physical exercise [29]. In these studies, intent can be interpreted as the anticipation of target behavior motivations in order to enhance efficiency and accuracy. Additionally, many scholars have conducted research on intent prediction. For example, [PERSON] et al. proposed an HRC digital modeling method that generates virtual HRC data using dynamic musculoskeletal models to predict human behavior intent [30]. [PERSON] et al. proposed a novel hierarchical mode transition control (NHMTC) algorithm that uses traffic information to calculate the required clutch engagement speed based on predicted driving intent, thereby improving the dynamic performance and ride comfort of connected hybrid electric vehicles (CHEVs) [31]. In linguistics, intent involves uncovering the purpose and meaning of conveyed language information. [PERSON] et al. proposed an automated model for detecting cyberbullying intentions that combines natural language processing techniques with machine learning methods [32]. [PERSON] et al. developed a geographic semantics-based method for identifying map retrieval intentions, which enhances the accuracy of map retrieval through an intention-driven approach [33]. [PERSON] et al. proposed a multimodal brain-computer fusion network (MBCFNet) that integrates acoustic, textual, and electroencephalogram (EEG) data to distinguish different intentions under the same textual information. Experimental results demonstrated that this method performs excellently in spoken intention recognition [34]. Additionally, neuromarketing and physiological measurements (such as fMRI, EEG, and eye tracking) are used to study users' cognitive and emotional responses to products, helping designers understand user needs and optimize design and decision-making processes. For instance, [PERSON] et al. discussed the potential applications of functional magnetic resonance imaging (fMRI) in cognitive design science and proposed recommendations for developing experimental protocols, constructing cognitive design ontologies, and generating brain activations, thereby advancing the scientific application of fMRI in design research [35]. [PERSON] et al. applied a multimodal fusion strategy combining EEG and eye-tracking data to propose a decision-making method that reduces subjectivity and bias in design decisions [36]. These studies, through interdisciplinary integration of physiological signals, self-supervised learning methods, and domain knowledge in intent recognition, provide insightful approaches for exploring cognitive intent of form characteristics. [PERSON] et al. combined deep learning with Dempster-Shafer theory to propose an information fusion method for global target intent recognition under uncertain and incomplete information [37]. [PERSON] et al. developed a recursive convolutional neural network model based on a non-invasive brain-computer interface (BCI) system to decode user intent from EEG signals [38]. However, current research on the application of cognitive intent in product form characteristics still has limitations. Firstly, these methods primarily focus on specific application scenarios, making it challenging to directly transfer them to the field of product design. Secondly, existing methods often lack a comprehensive understanding and prediction of users' cognitive intent regarding form, encompassing both emotional and rational cognition. Although there is limited research on the cognitive intent of product form design, the ideas and methods from intent research in other fields can provide insights and references for this study.