Electronic File Management Method and System Based on Machine Learning Algorithm

Han Debin, Song Ruolin, Zhang Yue

2025

Abstract

In order to meet the challenges of electronic archives management methods and systems, this study proposes an innovative archives management method and system method based on machine learning algorithms in view of the shortcomings of the existing whale algorithms. The new approach leverages the theoretical principles of computer science to pinpoint and locate key influencing factors, and accordingly intelligently classifies indicators to reduce potential interference. At the same time, using the unique mechanism of machine learning algorithms, this scheme cleverly constructs the design strategy of management methods. The empirical results show that this scheme shows a significant improvement compared with the traditional whale algorithm in key performance indicators such as the accuracy of the file management method and system, and the processing efficiency of key factors, showing its obvious strong advantages. In electronic archives, archives management methods and systems play a vital role, which can accurately predict and optimize the growth trend and output results of electronic archives management methods and systems. However, in the face of complex simulation tasks, traditional whale algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this problem, this study introduces a new idea of file management method and system optimized by machine learning algorithm, accurately controls the influencing parameters through computer science theory, and uses this as a road map for indicator allocation, and then uses machine learning algorithm to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the electronic file management method and system, the simulation scheme based on machine learning algorithm successfully overcomes the shortcomings of the traditional whale algorithm and significantly improves the accuracy and operation efficiency of the simulation.

Paper Citation

in Harvard Style

Debin H., Ruolin S. and Yue Z. (2025). Electronic File Management Method and System Based on Machine Learning Algorithm. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 25-31. DOI: 10.5220/0013534800004664

in Bibtex Style

@conference{incoft25,
author={Han Debin and Song Ruolin and Zhang Yue},
title={Electronic File Management Method and System Based on Machine Learning Algorithm},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={25-31},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013534800004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Electronic File Management Method and System Based on Machine Learning Algorithm
SN - 978-989-758-763-4
AU - Debin H.
AU - Ruolin S.
AU - Yue Z.
PY - 2025
SP - 25
EP - 31
DO - 10.5220/0013534800004664
PB - SciTePress