Research on Comprehensive Evaluation Model of Cultural Value of Tang Dynasty Literary Works Based on Convolutional Neural Network and Text Feature Extraction
Bibliographic record
Abstract
In the process of social development of Tang Dynasty, literary works behind the depth of interpretation and expression, systematized spiritual concepts.In this paper, the text data of Tang Dynasty literary works are processed by word division and de-discontinued words, and it is intended to use Transformer model to realize the word vector transformation of text data, and put the word vector into Text-CNN network for iterative training to realize the text feature extraction.By means of text feature screening, the cultural value assessment system of Tang Dynasty literary works is formed, and a comprehensive evaluation model of cultural value is designed under the role of convolutional neural network and text features, and using the model of this paper, the cultural value of Tang Dynasty literary works is assessed.The accuracy rate of cultural value classes "", "" and "" is 1, while the accuracy rate of cultural value classes "I" and "" have accuracy rates of 0.98 and 0.96, indicating that the model in this paper can accurately assess the cultural value in Tang Dynasty literary works.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".