Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 6, Issue 2


Published
on

May 20, 2022


Pages

2345-2356


DOI

Article

Research on the mining of ideological and political knowledge elements in college courses based on the combination of LDA model and Apriori algorithm

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Authors

Long Wang Affiliation:
Shaoyang University, Shaoyang, Hunan 422099, China


Abstract

In recent years, mapping of knowledge domain and political knowledge has developed rapidly and gradually penetrated into many practical applications. The construction of an ideological and political knowledge framework for colleges has become one of the important applications. Therefore, commencing with the theory of mapping of knowledge domain, and aiming at resolving the difficulty involved in effectively extracting and analysing ideological and political knowledge, a mining method for ideological and political knowledge elements is constructed in this paper, based on LDA model and Apriori algorithm. By setting a three-dimensional matrix of keywords and association rules, an algorithm for mining of ideological and political knowledge elements is proposed, where LDA model is used to acquire ideological and political subject words, and Apriori algorithm is used to discover tacit ideological and political knowledge. It is helpful to solve the problem of excavation and presentation of ideological and political elements in college curriculum, serve the ideological and political construction of curriculum, which is of great significance to dredge students’ learning paths and reduce learners’ learning cost.


Keywords

Mapping of knowledge domain, LDA model, Apriori algorithm, Ideological and political knowledge element


Citation

Wang, L. (2021). Research on the mining of ideological and political knowledge elements in college courses based on the combination of LDA model and apriori algorithm. Applied Mathematics and Nonlinear Sciences, 6(2), 2345–2356. https://doi.org/10.2478/amns.2021.2.00190

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