Evidence map›Paper›PMID 39811381›Full record

ArticleHeliyon2025

The construction of a digital dissemination platform for the intangible cultural heritage using convolutional neural network models.

Zhurong Liu

Abstract read
In one paragraph

Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Zhurong LiuSchool of International Tourism and Culture, Guizhou Normal University, Guiyang, 550025, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to promote the digital dissemination and preservation of Chinese intangible cultural heritage, this work constructs a digital platform for its transmission. The platform integrates a range of advanced technologies, including the Densely Connected Convolutional Networks - Bottleneck and Compression model, a notable convolutional neural network, along with natural language processing algorithms, generative adversarial network algorithms, and neural collaborative filtering algorithms. The platform is validated with 224,055 publicly archived valid data records, ensuring its effectiveness and reliability. The results indicate that the heteroscedasticity-robust standard error is 0.15, and the platform achieves accuracies of 0.87 and 0.89 in 5-fold and 10-fold cross-validation, respectively, demonstrating the model's stability and predictive accuracy. Under various operating conditions, the platform's accuracy, recall, and F1-Score all exceed 0.85, with a root mean square error below 0.12, ensuring efficient and reliable recommendation performance. Regarding user experience, page load times remain steady between 6.45 and 10.25 s, with bounce rates and error rates controlled between 40.88 % to 58.33 % and 0.05 %-0.18 %, respectively. A/B testing and heatmap analysis indicate that optimizing page layout significantly improves click-through and conversion rates. Additionally, in the fields of oral literature and traditional medicine, the platform achieves entity recognition accuracies of 90.2 % and 91.3 %, respectively. The platform also demonstrates strong coverage in the knowledge of folklore and traditional crafts, with rates reaching 92.5 % and 89.8 %, respectively, showcasing its advantages across different domains. This work aims to integrate cultural heritage conservation, cultural inheritance, and digital dissemination, fostering the broad transmission and preservation of intangible cultural heritage in the era of big data.

Indexed as

Bottleneck and compression modelConvolutional neural networkCultural inheritanceDensely connected convolutional networksDigital dissemination platformIntangible cultural heritage

Identifiers

PMID39811381
PMCPMC11730209

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.