Research on Intelligent Retrieval Method of Teaching Resources on Large-Scale Network Platform

With the increase in information on various cloud computing platforms, there are more and more teaching documents and videos, which provide sufficient resources for people to learn. Facing the large-scale digital teaching resources, how to quickly and accurately retrieve the required content has bec...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2022-05, Vol.2022, p.1-8
Main Authors: Su, Xiaofeng, Zhang, Tianjing
Format: Article
Language:English
Subjects:
Ant colony optimization
Cloud computing
College campuses
Data storage
Engineering
Evolution & development
Genetic algorithms
Information retrieval
Internet
Keywords
Network topologies
Optimization techniques
Query expansion
Random numbers
Relevance feedback
Retrieval
Search engines
Semantics
Teaching
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the increase in information on various cloud computing platforms, there are more and more teaching documents and videos, which provide sufficient resources for people to learn. Facing the large-scale digital teaching resources, how to quickly and accurately retrieve the required content has become an important research direction in the information field. Especially in the face of heterogeneous, dynamic, and large-scale teaching resources stored in the cloud computing platform, the traditional cloud computing resource retrieval has poor performance and low work efficiency. To solve this problem, a cloud computing platform retrieval method based on genetic algorithm is proposed, which is suitable for intelligent retrieval of teaching resources. Firstly, the teaching resource storage system based on cloud computing platform is analyzed, and the overall architecture of the system and the network topology of cloud storage data are given. Then, a resource retrieval method suitable for cloud computing platform is designed by genetic algorithm, and the convergence performance of genetic algorithm is improved by ant colony algorithm. Finally, the selection algorithm in genetic algorithm is optimized by using random numbers and increasing the number of cycles. The experimental results show that the proposed intelligent retrieval method has greatly improved the Recall and Precision compared with the traditional retrieval methods.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/2745773