Exploring the Path of Intangible Cultural Heritage and Protection Promoted by Artificial Intelligence: Taking the Eight Wonders of Yanjing as an Example
Bibliographic record
Abstract
The article explores the application path of artificial intelligence technology in the inheritance and protection of intangible cultural heritage. Specifically, by constructing digital archives of intangible cultural heritage, we can utilize virtual reality and augmented reality technologies to enrich the interactive experience of intangible cultural heritage; Meanwhile, with the help of big data analysis and user behavior analysis, we can achieve precise dissemination of content related to intangible cultural heritage. Artificial intelligence has also promoted the innovative design and intelligent production of intangible cultural heritage products, broadening the inheritance path of intangible cultural heritage. Taking the Eight Treasures of Yanjing as an example, this paper analyzes the challenges it faces and proposes specific measures for artificial intelligence in data recording, intelligent recognition, innovative design, intelligent dissemination, and protection and repair. Finally, research has shown that the deep integration of artificial intelligence technology provides new impetus for the inheritance and protection of intangible cultural heritage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".