Characteristics Analysis of Applied Mathematics in Colleges and Universities Based on Big Data Mining Algorithm Model

To analyze the mathematical nature of applied mathematics in colleges and universities, a method based on a model of big data mining algorithms is proposed. Firstly, the modeling is carried out through the deployment of nodes, which can accurately collect the characteristics of data information in t...

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Bibliographic Details
Published in:Security and communication networks 2022-05, Vol.2022, p.1-13
Main Authors: Wang, Yuqin, Tian, Feng, Bai, Yong
Format: Article
Language:English
Subjects:
Algorithms
Applications of mathematics
Applied mathematics
Artificial intelligence
Automation
Big Data
Colleges & universities
Construction
Data analysis
Data collection
Data compression
Data mining
Data processing
Digital economy
Discipline
Education
Information retrieval
Information sources
Internet
Kernel functions
Mathematical analysis
Project management
Support vector machines
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Summary:To analyze the mathematical nature of applied mathematics in colleges and universities, a method based on a model of big data mining algorithms is proposed. Firstly, the modeling is carried out through the deployment of nodes, which can accurately collect the characteristics of data information in the case of massive big data; secondly, the acquisition algorithm of multi-feature fusion is systematically optimized, which can avoid data interference and collect features quickly and accurately; thirdly, by transforming the multidimensional application-oriented university applied mathematics discipline model into an unlimited experience loss minimization problem with penalty factors, the improved support vector machine algorithm is used to construct and solve the objective kernel function. It is proved that the intelligent collection method based on big data mining algorithm model for the characteristics of applied mathematics in colleges and universities is effective. The sympathetic set corresponding to the flowing data is [115∼135]; the data similarity in big data environment is 1; in order to ensure that the intelligent collection method of applied mathematics discipline characteristics in colleges and universities based on big data analysis can collect data characteristics more accurately, which are 110/76.65/78/110, respectively.
ISSN:1939-0114
1939-0122
DOI:10.1155/2022/7978031