Research on Cultural Construction of Spring Festival Gala Mascot Using Intelligent Computing Model – Brand Innovation and Communication Path of Chinese Intangible Cultural Heritage
Bibliographic record
Abstract
This study focuses on the construction of Spring Festival Gala mascot culture using intelligent computational modeling, so as to explore the brand innovation and communication path of Chinese intangible cultural heritage.The Apriori algorithm is utilized to extract the features of intangible cultural heritage in the mascot design, and at the same time, the association rules between different intangible cultural heritage features are mined and integrated into the design.The traditional Apriori algorithm is improved based on Boolean matrix and adaptive updating support calculation strategy to ensure its effectiveness and innovativeness for mascot design.Combined with the theory of propagation dynamics, the propagation model of this paper is constructed by adding the node of latent propagator on the basis of the traditional model of infectious disease (SIR).And in order to enhance the influence of the mascot in the communication network, this paper proposes a mascot accurate recommendation model for its further dissemination.The research results show that the method of this paper can effectively extract the non-heritage cultural features and association rules in the Spring Festival Gala mascot, and the Spring Festival Gala mascot designed by the method of this paper can ensure high economic benefits under the premise of high quality.In addition, the communication model and precise recommendation method constructed in this paper can also give full play to the communication role and effectively communicate the Spring Festival Gala mascot and the non-heritage cultural elements it carries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".