2026 Vol.9 Feb.N01   

• Title: Intelligent retrieval method of ideological and political materials for computer specialty courses based on keywords
• Name: Li Cao , Qiuyue Niu , Shan Jiang
• Company: School of Information Technology, Henan University of Chinese Medicine，Zhengzhou，450000,China
• Abstract:

  Traditional retrieval methods for ideological and political education materials in computer science courses primarily relied on manual annotation or superficial semantic matching. These approaches suffered from high subjectivity, slow updates, and inability to accommodate technical terminology, resulting in reduced precision and recall rates. To address this, an intelligent retrieval system was proposed that automatically identifies relevant materials after keyword determination. The method first integrates multi-source materials to establish a structured database as the foundational data. It then employs the TF-IDF algorithm to extract keywords from the materials. Finally, the system matches these keywords against computer science course materials while calculating cosine similarity between keywords and documents, achieving intelligent retrieval. Experimental results demonstrate that the proposed method achieves precision and recall rates exceeding 95%, significantly outperforming traditional approaches. This indicates the method effectively meets the educational requirements for ideological and political materials in computer science courses.

  
  

• Keyword: Computer Science; Ideological and Political Education Materials; Intelligent Retrieval;
• DOI: 10.12250/jpciams2026090202
• Citation form: Li Cao , Qiuyue Niu , Shan Jiang.Intelligent retrieval method of ideological and political materials for computer specialty courses based on keywords[J]. Computer Informatization and Mechanical System,2026,Vol.9,pp.

[1] Fu Y. Retrieval method for online teaching resources of physics experiment courses in universities based on hybrid machine learning [J]. Information and Computer, 2024,36(5):251-253.

[2] Liu HZ, Zhang H. Image-text retrieval method based on semantic modulation and two-level matching [J]. Computer Technology and Development, 2024,34(12):100-107.

[3] Zhang YW, Liu T, Liu C, et al. Research on Text Corpus Retrieval Methods Integrating Syntactic Information [J]. Modern Library and Information Technology, 2022(11):25-37.

[4] Zhang Shufang, Li Yuhui, Li Bingzhi. Efficient random retrieval method for files based on primer index matrix in DNA storage scenarios [J]. Journal of Electronics and Information Science, 2024,46(6):2568-2577.

[5] Song T. Intelligent retrieval method for library literature resources based on decision tree[J]. Automation Technology and Application, 2024,43(4):63-66.

[6] Liu H. Online retrieval method for C language course teaching resources based on semantic similarity matching [J]. Information and Computer, 2023,35(15):234-236.

[7] Liang Yanchun, Ruan Yilong, Li Chenyang, et al. A resource retrieval method for internet platform data mid-platform based on hash algorithm [J]. Modern Transmission, 2024(2):37-40.

[8] Fu Xiaofei, Chen Han, Chen Lei, et al. Data resource retrieval method for multi-level scheduling management based on data middle platform [J]. Modern Computer, 2023,29(9):98-102.

[9] Xu Shuzheng. Research on personalized learning resource retrieval method for college employment education based on convolutional neural networks [J]. Information and Computer, 2023,35(20):127-129.

[10] Dai Yong, Yang Guoping, Hou Qing, et al. Text relevance information retrieval with multi-feature penalty factors [J]. Computer Simulation, 2025,42(07):211-215.