## 5 Generative Verification Across Platforms
### Expert Ratings and User Questionnaire Statistical Analysis
#### 5.4.2 Statistical Analysis of User Questionnaires
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dimensions** & \begin{tabular}{c} **Dreamina AI** \\ **Rating (DM)** \\ \end{tabular} & \begin{tabular}{c} **Midjourney** \\ **Rating (MJ)** \\ \end{tabular} & \begin{tabular}{c} **User Preference** \\ **Ratio (DM Is Better)** \\ \end{tabular} & \begin{tabular}{c} **User Preference** \\ **Ratio (MJ Is Better)** \\ \end{tabular} \\ \hline \begin{tabular}{l} Pixel configuration \\ expressiveness \\ \end{tabular} & 4.22 & 3.48 & 87.50\% & 12.50\% \\ \hline \begin{tabular}{l} Colour style consistency \\ \end{tabular} & 3.72 & 4.22 & 28.13\% & 71.88\% \\ \hline \begin{tabular}{l} Craft semantic \\ restoration ability \\ \end{tabular} & 3.58 & 4.02 & 29.69\% & 70.31\% \\ \hline \begin{tabular}{l} Cultural connotation \\ communication effect \\ \end{tabular} & 3.75 & 3.94 & 40.63\% & 59.38\% \\ \hline \
\begin{tabular}{l} Theme situation \\ expression accuracy \\ \end{tabular} & 4.01 & 3.88 & 54.69\% & 45.31\% \\ \hline
\begin{tabular}{l} Overall innovation and \\ design performance \\ \end{tabular} & 3.84 & 3.59 & 64.06\% & 35.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of key data of user ratings. shadow levels, and composition artistic conception design, and was more likely to trigger users' cultural associations. 5. Accuracy of thematic context expression. This dimension focuses on whether the image accurately restores the usage context and cultural scene it sets. For example, in the \"hundred birds paying homage to the phoenix\" theme composition (DM_0104 vs. MJ_0104), the Dreamina AI image could more fully express the cultural, artistic conception of many birds surrounding the phoenix and auspicious gatherings. The user score was slightly higher than [PERSON]'s, showing a higher semantic response ability in processing the traditional visual structure hierarchy. 6. Overall innovation and design performance. In the dimension of \"overall innovation and design expression\", this study focuses on the visual expressiveness of generated images in modern aesthetics, cultural integration, and contemporary packaging. According to the comparison results of images DM_0107 and MJ_0107 (see Figure 11), a total of 64 users participated in the voting, of which 64.06% of the respondents believed that the image DM_0107 generated by the Dreamina AI platform could better reflect the core image of \"Chinese traditional phoenix pattern culture\" in contemporary packaging design. In contrast, the image MJ_0107 generated by the Midjourney platform was recognized by 35.94% of users. This result shows that Dreamina AI has more performance advantages in the accuracy and integration of cultural semantic expression, especially in the context of processing the integration of traditional Chinese patterns and modern packaging visual language, and its generation results are more in line with users' cultural intuition and design expectations. In addition, in the aesthetic judgment of another set of images, DM_0108 and MJ_0108 (see Figure 12), Dreamina AI once again gained significant preference, with 67.19% of users believing that DM_0108 was more in line with the design aesthetics of contemporary tea packaging in their minds, while MJ_0108 only received 32.81%. This further verifies the semantic recognition and visual narrative tension demonstrated by Dreamina AI in expressing the fusion image of \"Phoenix Culture \(\times\) Tea Culture\". Comprehensively analysing the data of the two questions, Dreamina AI shows stronger cultural adaptability and design inspiration in terms of \"pixel fusion\", \"symbol innovation\", and \"scene adaptability\", highlighting the advantages of its Chinese pre-trained model in prompt semantic scheduling and scene reconstruction.