Design and simulation of an algorithm for evaluating the acceptance effect of online literature based on data mining technology

Dandan Sun; Linghua He

doi:10.1117/12.3033296

5 July 2024 Design and simulation of an algorithm for evaluating the acceptance effect of online literature based on data mining technology

Dandan Sun, Linghua He

Author Affiliations +

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318431 (2024) https://doi.org/10.1117/12.3033296
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

The aim of this study is to construct a network literature acceptance evaluation model based on data mining technology, deeply explore the reading behavior characteristics, preferences, and interaction patterns of readers, and effectively evaluate the acceptance effect of literary works and optimize content creation and promotion strategies. The core methodology includes utilizing the FP Growth algorithm to mine frequent data items and improving the processing efficiency and accuracy of the evaluation model through the PFP parallel computing system. This study not only provides a new methodology for the quantitative analysis of online literature, but also provides a scientific basis for the quality evaluation and cultural value dissemination of literary works. Through algorithm design and simulation testing, this article deeply explores the potential and challenges of data mining technology in the field of online literature evaluation, providing theoretical support and practical guidance for the healthy development and innovation of online literature.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Dandan Sun and Linghua He "Design and simulation of an algorithm for evaluating the acceptance effect of online literature based on data mining technology", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318431 (5 July 2024); https://doi.org/10.1117/12.3033296

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KEYWORDS

Data mining

Data processing

Computing systems

Parallel computing

Data modeling

Computer simulations

Mining

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