Research on Key Information Retrieval Method of Complex Network Based on Artificial Intelligence

  • Bozhong LiuEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 301)


Aiming at the problem of poor retrieval accuracy and slow retrieval speed of information retrieval method based on hyperlink, a key information retrieval method based on artificial intelligence was proposed. The method was mainly divided into three steps, and each step was completed with the help of artificial intelligence. First, file information was preprocessed (information processing and information filtering), then keywords were extracted from information content, and finally semantic similarity calculation and semantic information matching were conducted to complete key information retrieval in complex networks. The results showed that the accuracy of key information retrieval method of complex network based on artificial intelligence was improved by 2.27% and the speed of retrieval was improved by 3.06 s.


Artificial intelligence Complex networks Information retrieval Keywords extraction Semantic similarity Semantic information matching 


  1. 1.
    Zanganeh, M.Y., Hariri, N.: The role of emotional aspects in the information retrieval from the web. Online Inf. Rev. 42(4), 520–534 (2018)CrossRefGoogle Scholar
  2. 2.
    Ou, S., Tang, Z., Su, J.: Construction and usage of terminology services for information retrieval. J. Libr. Sci. China 42(2), 32–51 (2016)Google Scholar
  3. 3.
    Wang, L.: Text information retrieval algorithm simulation analysis under massive data. Comput. Simul. 33(4), 429–432 (2016)Google Scholar
  4. 4.
    Liu, J., Cao, S.: Analysis on the information retrieval experience: a perspective of flow theory. Libr. Inf. Serv. (8), 67–73 (2017)Google Scholar
  5. 5.
    Cai, J., Tang, Y.: A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval. Neural Netw. Official J. Int. Neural Netw. Soc. 98, 178 (2017)CrossRefGoogle Scholar
  6. 6.
    Huang, Y.Y., Li, Y.: Research on cognitive information retrieval model based on the “scarcity theory”. J. Intell. 35(11), 136–140 (2016)Google Scholar
  7. 7.
    Ashwin, K.T.K., Thomas, J.P., Parepally, S.: An efficient and secure information retrieval framework for content centric networks. J. Parallel Distrib. Comput. 104, 223–233 (2017)CrossRefGoogle Scholar
  8. 8.
    Kralj, J., Robnik-Šikonja, M., Lavrač, N.: HINMINE: heterogeneous information network mining with information retrieval heuristics. J. Intell. Inf. Syst. 50, 29–61 (2017)CrossRefGoogle Scholar
  9. 9.
    Cheng, Y., Lai, M.: Research on the information retrieval model based on D-S theory. Libr. Inf. Serv. 61(21), 5–12 (2017)Google Scholar
  10. 10.
    Liu, P., Feng, M., Ming, L.: Practical k-agents search algorithm towards information retrieval in complex networks. World Wide Web 1, 1–21 (2018)Google Scholar
  11. 11.
    Craswell, N., Croft, W.B., Rijke, M.D., et al.: Neural information retrieval: introduction to the special issue. Inf. Retrieval J. 21(2–3), 1–4 (2017)Google Scholar
  12. 12.
    Kishore, C.B.D.J., Reddy, T.B.: An efficient approach for land record classification and information retrieval in data warehouse. Int. J. Comput. Appl. 1–10 (2018)Google Scholar
  13. 13.
    Min, Y., Duan, J., Huang, M.: Research on information retrieval and intelligent fusion based on cloud computing environment. Modern Electron. Tech. (6), 162–164 (2018)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringGuang’an Vocational Technical CollegeGuang’anChina

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