Research on evaluation and prediction methods of cognitive intentions for product morphological features

The morphological characteristics of a product serve as essential carriers for conveying design intentions. These characteristics directly affect users’ comprehension of the product’s functions and proper usage, which are critical to the safety of product utilization and the overall comfort of the user experience. Incorporating prior experience to predict users’ cognitive intentions regarding product form characteristics can provide valuable evaluation and decision-making references for design. This approach effectively reduces product development risks and contributes to enhancing user acceptance and experience. The study established intention discrimination indicators for form characteristics, covering six dimensions: functional orientation, behavioral intention, recognizability, cognitive load, attention distribution, and experiential feeling. Combining multidimensional scaling (MDS) and systematic clustering, samples were screened, and the morphological decomposition method was used to categorize and extract form characteristic categories and feature factors. The entropy weight method was applied to assign weights to the feature categories, and a feedforward neural network (FNN) was employed to construct a prediction model for cognitive intentions regarding product form characteristics. The model was tested using leave-one-out cross-validation, yielding a mean squared error (MSE) of 0.0089 and an R correlation coefficient of 0.9998, indicating high reliability. Finally, the feasibility and effectiveness of this method were validated through a case study on earthquake science experience facilities.