## 5 Generative Verification Across Platforms
### Expert Ratings and User Questionnaire Statistical Analysis
#### 5.4.2 Statistical Analysis of User Questionnaires  
At the same time, user evaluation questionnaires were distributed through the questionnaire platform, and 64 valid samples were collected. The user groups included teachers/students (56.25%), designers (17.19%), cultural researchers, and the general public, ensuring that the feedback perspectives were diverse and representative. In order to comprehensively evaluate the semantic restoration ability and aesthetic output quality of the \"Phoenix Pattern Multidimensional Cultural Gene Model\" in the AIGC image generation process, this study constructed an evaluation system from six core dimensions, combined with user rating data and preference choices, and systematically  
Figure 10: Comparison of generated designs DM_0206 and MJ_0206. compared the performance differences in image expression between the Dreamina AI and Midjourney platforms; the details are shown in Table 8.  
1. Pixel configuration expressiveness. This dimension focuses on restoring the phoenix pattern at the \"composition pattern\" and \"pixel form\" levels. In the sample images represented by the \"curling grass-style long-tailed phoenix\" (DM_0101 vs. MJ_0101), the image DM_0101 generated by Dreamina AI scored 4.22 (out of 5 points), significantly better than MJ_0101 (3.48). A total of 87.5% of users chose the DM image as the sample with \"more accurate pixel expression\", believing that it more faithfully restored the layered structure of the phoenix tail and the bending trend of the feathers. This result shows that Dreamina AI has a strong ontological knowledge recognition ability when dealing with traditional pixel contour structure tasks. 2. Colour style consistency. This dimension evaluates the accuracy and harmony of the image in colour control and aesthetic tonality. In the \"five-colour phoenix\" colour sample (DM_0202 vs. MJ_0202), the MJ image scored 4.22, better than DM's 3.72. Users generally reported that Midjourney performed better in metal lustre processing and colour level transition, especially in showing the dynamic tension under the high saturation combination of red, blue, and gold. A total of 71.88% of users thought the MJ platform was more suitable for expressing traditional pattern colour styles with high colour impact and complex tonality. 3. Craft semantic restoration ability. This dimension focuses on the reproduction effect of the texture and structural semantics of the image in traditional materials (such as jade carving, wood carving, embroidery, cloisonne enamel, etc.). In the \"wood carving craft\" expression task (DM_0103 vs. MJ_0103), the MJ image scored 4.02 points higher than DM's 3.58. About 70.31% of users believed that the MJ image was closer to real wood carving in terms of texture clarity and three-dimensional layering. This phenomenon reflects that the Midjourney platform has a stronger visual rendering engine and parameter refinement capabilities in processing complex material details. 4. Cultural implication communication effect. This dimension examines whether the generated image can accurately reflect the cultural symbols and spiritual implications involved in the prompt. For example, in comparing image samples under the \"classical charm\" (DM_0105 vs. MJ_0105), the MJ image scored 3.94, slightly higher than DM's 3.75. Although the scores of the two are close, experts believed that the MJ image had more \"oriental artistic conception\" in terms of background blur, light and