## 5 Conclusions  
This paper takes the cognition and prediction of product morphological feature design intent as the research perspective and establishes indicators for the cognitive intent discrimination of morphological feature categories and feature factors. The research process covered the collection of representative samples, selection of typical samples, division of morphological feature categories and decomposition of feature factors, calculation of weights, and comprehensive evaluation of cognitive intent. A feedforward neural network cognitive intent prediction model was constructed, and leave-one-out cross-validation was used to improve the model accuracy. Finally, combined with practical design cases of earthquake science experience facilities, cognitive intent prediction was carried out to verify the reliability and effectiveness of the prediction model. This provides a scientific basis for design decisions, evaluation, and optimization, thereby helping to reduce the risk and cost of facility development. First, the study established a set of morphological feature intention evaluation indicators covering six dimensions: functional orientation, behavioral intention, recognizability, cognitive load, attention distribution, and experiential perception. This provides a standard for evaluating design intentions. Second, a sample selection method that integrates multidimensional scaling (MDS) and systematic clustering was proposed, and morphological decomposition employed to deconstruct and extract morphological feature categories and feature factors. Additionally, the entropy weight method was used to determine the weights of the feature categories. Furthermore, a product morphological feature cognitive intention prediction model was constructed based on a feedforward neural network (FNN), and its reliability was tested using leave-one-out cross-validation. Finally, using earthquake science experience facilities as an application case, five typical samples were extracted from 35 samples using MDS and systematic clustering methods. Combining six cognitive \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Conceptual Design Schemes** & **Morphological Feature Encoding** & **Prediction Results** & **Cognitive Intent Evaluation Level** \\ \hline \hline \(F_{1}\) & & 10000 001 0100 100 & 70.2807 & Moderate \\ \hline \(F_{2}\) & & 00001 010 00001 010 & 78.3589 & Fair \\ \hline \(F_{3}\) & & 01000 100 00100 001 & 81.3025 & Strong \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Conceptual design schemes and cognitive intent prediction for earthquake science experience facilities. intention evaluation indicators, such as functional orientation, behavioral intention, and recognizability, the morphological features of the earthquake science experience facilities were divided into 6 feature categories and 25 feature factors. The samples were then one-hot encoded, and comprehensive cognitive intention evaluation and interval division were performed to construct 35 training samples. An FNN prediction model was established with a mean squared error (MSE) of 0.0089, verifying the model's high accuracy. Additionally, the study predicted the cognitive intention scores for three earthquake science experience facility design schemes, with results of 70.2807, 78.3589, and 81.3025, respectively, demonstrating the proposed method's strong usability and effectiveness. This model can accurately capture and predict users' cognitive intentions regarding product morphology, providing a scientific basis for design and decision-making. This research not only fills a theoretical gap in the study of intention in the field of design but also provides a scientific design tool for practitioners. With this tool, designers can better understand user needs and create products that align more closely with user expectations, thereby enhancing product satisfaction and market competitiveness. Some limitations and future work of this study are as follows.