## 4 Construction of Morphological Cognitive Intent Prediction Network Model
### Cognitive Intent Prediction Application for Conceptual Design Schemes  
The basic form of the earthquake science experience facility could be divided into six feature categories, each containing three to five feature factors. Through different combinations, \(5\times 3\times 5\times 3\times 4\times 5=4500\) design schemes could be generated. In the earlier stages of model training, relevant data on these morphological features were already included. Therefore, the model was capable of predicting and evaluating the cognitive intent strength of new schemes. Conceptual design schemes were encoded and passed as outputs into the prediction model. Upon execution, the prediction results were obtained.  
These predictions involved three design schemes, aiming to achieve reasonable functional settings, strong cognitive intent in form perception and aesthetic appearance, and comfortable user experience. These schemes were conceptually designed by three designers, combining relevant feature factors. The morphological features of the three schemes were encoded and inputted into the prediction model, as shown in Table 13. The final comprehensive cognitive intent evaluation scores for the three schemes were 70.2807, 78.3589, and 81.3025. It is evident that scheme \(F_{3}\) exhibited the strongest cognitive intent, indicating that its design intent was accurately conveyed and the functional intent conveyed by its form  
Figure 10: Comparison of predicted results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Actual Value** & **Predicted Value** & **Error** & **Sample** & **Actual Value** & **Predicted Value** & **Error** \\ \hline \(S_{1}\) & 81.9133 & 81.8726 & 0.0407 & \(S_{19}\) & 76.3433 & 76.4739 & \(-\)0.1306 \\ \(S_{2}\) & 73.4589 & 73.5141 & \(-\)0.0552 & \(S_{20}\) & 73.6411 & 73.5658 & 0.0753 \\ \(S_{3}\) & 69.2411 & 69.1700 & 0.0711 & \(S_{21}\) & 76.3433 & 76.3496 & \(-\)0.0063 \\ \(S_{4}\) & 80.8700 & 80.8562 & 0.0138 & \(S_{22}\) & 77.8667 & 77.8741 & \(-\)0.0074 \\ \(S_{5}\) & 74.6967 & 74.7691 & \(-\)0.0724 & \(S_{23}\) & 72.0122 & 71.9571 & 0.0551 \\ \(S_{6}\) & 80.3222 & 80.1983 & 0.1239 & \(S_{24}\) & 71.0533 & 71.1193 & \(-\)0.0660 \\ \(S_{7}\) & 80.7311 & 80.8026 & \(-\)0.0715 & \(S_{25}\) & 72.4533 & 72.4368 & 0.0165 \\ \(S_{8}\) & 68.3244 & 68.2990 & 0.0254 & \(S_{26}\) & 79.7400 & 79.7503 & \(-\)0.0103 \\ \(S_{9}\) & 85.4233 & 85.3421 & 0.0812 & \(S_{27}\) & 72.3978 & 72.3843 & 0.0135 \\ \(S_{10}\) & 81.0222 & 81.0388 & \(-\)0.0166 & \(S_{28}\) & 74.1122 & 74.1102 & 0.0020 \\ \(S_{11}\) & 67.3544 & 67.3649 & \(-\)0.0105 & \(S_{29}\) & 73.2689 & 73.2536 & 0.0153 \\ \(S_{12}\) & 78.0778 & 78.0485 & 0.0293 & \(S_{30}\) & 72.7178 & 72.6828 & 0.0350 \\ \(S_{13}\) & 74.9944 & 74.9122 & 0.0822 & \(S_{31}\) & 75.5422 & 75.2019 & 0.3403 \\ \(S_{14}\) & 67.4211 & 67.6231 & \(-\)0.2020 & \(S_{32}\) & 78.6156 & 78.6649 & \(-\)0.0493 \\ \(S_{15}\) & 74.7211 & 74.9429 & \(-\)0.2218 & \(S_{33}\) & 72.7744 & 72.4806 & 0.2938 \\ \(S_{16}\) & 84.7822 & 84.6595 & 0.1227 & \(S_{34}\) & 71.1956 & 71.2710 & \(-\)0.0754 \\ \(S_{17}\) & 69.2378 & 69.2039 & 0.0339 & \(S_{35}\) & 83.3700 & 83.4852 & \(-\)0.1152 \\ \(S_{18}\) & 84.9933 & 84.8799 & 0.1134 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: Errors between actual values and predicted values.  
was strong, making it easy for users to identify with low cognitive load and high comprehensive. Additionally, the design scheme had strong appeal and high acceptance. These results demonstrate that the prediction model can assess the design intent of schemes in the creative design stage, assisting designers in making correct decisions, thereby improving design efficiency and reducing research and development costs and risks.