Analysis of Structured Data Processing and Aesthetic Experience in Classical Dance Theatre Works
Bibliographic record
Abstract
Dance drama is a comprehensive art with dramatic conflicts and plots based on the use of dance’s own language system, which plays an important role in cultural dissemination and aesthetic experience. The article designs a resource library of classic dance drama works in the way of WEB site, establishes a data dynamic distribution strategy to deal with structured data, and combines the consistent hash algorithm to optimize the load balancing of structured data in the resource library. Then, a graph convolutional neural network model and a sample-weighted aesthetic classification model are combined to establish an aesthetic assessment model for images of classical dance drama works, and a regularized matching module is designed. For the application effectiveness of the structured data processing strategy, the structured data processing of the classic dance and drama works resource library is verified, and the hyperparameters of the model, evaluation results and ablation experiments are also analyzed. Combined with the data in the resource library of classic dance drama works, the aesthetic experience of the audience was analyzed using a questionnaire. After using the dynamic distribution strategy to process the structured data, its write and query times were shortened by 40.05% and 17.89% compared to before use, and the response time under different index query load balance degrees did not exceed 55ms.The accuracy of the aesthetic assessment model for classical dance and drama works was 48.85%, and the accuracy improvement of the G-AANet model compared to BoTNet ranged from 0.93% ~ between 6.12%. The resource base of classical dance drama works established through structured data processing helps to enhance the audience’s aesthetic experience of dance drama works and helps them to appreciate the spiritual connotation of dance drama works.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".