The Construction of Wushu Cultural Communication System Driven by Intelligent Technology in the Age of Big Data
Bibliographic record
Abstract
As an important part of the excellent traditional culture of the Chinese nation, Chinese Wushu condenses the wisdom of the Chinese nation, contains the genes of Chinese culture, and has important communication value.Based on the big data Hadoop technology, the article proposes a content recommendation design scheme for all-media communication of wushu cultural communication, and introduces the LFM model and MBGD algorithm to construct an intelligent recommendation model of wushu cultural communication content under the framework.Then, based on Lasswell's 5W model, the fsQCA method was utilized to explore the relevant factors affecting the effect of martial arts cultural communication.When the number of hidden factors of LFM-MBGD intelligent recommendation model is 55, its RMSE is 0.92, and the HR@K value of the model can reach 62.12%.The consistency level of the existence and non-existence states of each conditional variable of the communication effect of wushu culture is less than 0.8, and the overall coverage rate and the coverage rate of each path are higher than 0.85.The wushu culture communication system driven by intelligent technology can start from building an online resource base of wushu culture, broadening the communication paths of wushu culture, sounding the laws and regulations of wushu culture communication, and building the brand of wushu to improve the communication effect of wushu culture.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".