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<p>This study focuses on the problem of efficient classification of university English teaching resources, aiming to improve the ease of browsing and utilization of resources. To address the challenges of existing resource classification, the study proposes a label generation classification algorithm with multi-feature fusion. The algorithm incorporates TF-IDF and location information weights based on TextRank to generate labels containing corpus and location information. The performance of the algorithm is tested and simulation experiments are conducted. The study results show that the mean value of the label generation classification algorithm after multi-feature fusion reaches 0.234, 0.6219, and 0.3632 in terms of accuracy, recall, and F-value, respectively. In the simulation experiments, the algorithm achieves a classification speed of 90.33% faster than that of the traditional single-feature classification algorithm, and the classification error rate is no more than 10%. The label generation classification algorithm has a significant advantage in overall performance and effectively solves the problem of classifying university English teaching resources. This finding is of great significance for improving the utilization of teaching resources and optimizing the management of teaching resources.</p>
</abstract>

This study focuses on the problem of efficient classification of university English teaching resources, aiming to improve the ease of browsing and utilization of resources. To address the challenges of existing resource classification, the study proposes a label generation classification algorithm with multi-feature fusion. The algorithm incorporates TF-IDF and location information weights based on TextRank to generate labels containing corpus and location information. The performance of the algorithm is tested and simulation experiments are conducted. The study results show that the mean value of the label generation classification algorithm after multi-feature fusion reaches 0.234, 0.6219, and 0.3632 in terms of accuracy, recall, and F-value, respectively. In the simulation experiments, the algorithm achieves a classification speed of 90.33% faster than that of the traditional single-feature classification algorithm, and the classification error rate is no more than 10%. The label generation classification algorithm has a significant advantage in overall performance and effectively solves the problem of classifying university English teaching resources. This finding is of great significance for improving the utilization of teaching resources and optimizing the management of teaching resources.

A Classification Approach for University English Teaching Resources Based on Multi-Feature Fusion

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study focuses on the problem of efficient classification of university English teaching resources, aiming to improve the ease of browsing and utilization...

A Classification Approach for University English Teaching Resources Based on Multi-Feature Fusion

Published Online: Feb 26, 2024

Page range: -

Received: Jan 10, 2024

Accepted: Jan 15, 2024

DOI: https://doi.org/10.2478/amns-2024-0498

Keywords
Multi-feature fusion, TextRank, TF-IDF weights, Location information weights, Teaching resources classification

© 2024 Yiming Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Classification Approach for University English Teaching Resources Based on Multi-Feature Fusion

Published Online: Feb 26, 2024

Page range: -

Received: Jan 10, 2024

Accepted: Jan 15, 2024

DOI: https://doi.org/10.2478/amns-2024-0498

KeywordsMulti-feature fusion, TextRank, TF-IDF weights, Location information weights, Teaching resources classification

© 2024 Yiming Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Multi-feature fusion, TextRank, TF-IDF weights, Location information weights, Teaching resources classification