<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper integrates data mining technology into the technical processing layer of the “Artificial Intelligence +” archive intelligent service system to provide technical support for massive archive data. Using association rules, archive classification, Mean-Shift clustering, and other techniques to reveal the relationship between archives and users, archives, archives, and various practical activities so as to play the role of the information of these archive data at a deeper level. Applying the “Artificial Intelligence +” archive wisdom service system to the actual operation of the business management of college archive professional teachers, the results show that the speed of “Artificial Intelligence +” database entry (4.83s) is significantly higher than that of the traditional database (38.62s) the speed is higher. The contour coefficients of the classification results of the experimental group are all above 0.80, realizing high-precision classification of text data, image data, and audio data in digitized archives.2018-2023 The number of business declarations is rising, the total score of the scientific research type varies considerably, and the total score of the teaching type is concentrated in the distribution, with most of them being between 70-80. Mean-shift clustering algorithms can make the scientific research type, the teaching type, and the Research and Teaching and heavy type three data types to achieve a good clustering effect.</p>
</abstract>

This paper integrates data mining technology into the technical processing layer of the “Artificial Intelligence +” archive intelligent service system to provide technical support for massive archive data. Using association rules, archive classification, Mean-Shift clustering, and other techniques to reveal the relationship between archives and users, archives, archives, and various practical activities so as to play the role of the information of these archive data at a deeper level. Applying the “Artificial Intelligence +” archive wisdom service system to the actual operation of the business management of college archive professional teachers, the results show that the speed of “Artificial Intelligence +” database entry (4.83s) is significantly higher than that of the traditional database (38.62s) the speed is higher. The contour coefficients of the classification results of the experimental group are all above 0.80, realizing high-precision classification of text data, image data, and audio data in digitized archives.2018-2023 The number of business declarations is rising, the total score of the scientific research type varies considerably, and the total score of the teaching type is concentrated in the distribution, with most of them being between 70-80. Mean-shift clustering algorithms can make the scientific research type, the teaching type, and the Research and Teaching and heavy type three data types to achieve a good clustering effect.

A Study of the Impact of Integrating Artificial Intelligence with the Archival Profession under Data Mining

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper integrates data mining technology into the technical processing layer of the “Artificial Intelligence +” archive intelligent service system to...

A Study of the Impact of Integrating Artificial Intelligence with the Archival Profession under Data Mining

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 02. März 2024

Akzeptiert: 11. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1406

Schlüsselwörter
Artificial Intelligence+, Data Mining, Clustering, Archival Data

© 2024 Yue Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Study of the Impact of Integrating Artificial Intelligence with the Archival Profession under Data Mining

Online veröffentlicht: 07. Juni 2024

Seitenbereich: -

Eingereicht: 02. März 2024

Akzeptiert: 11. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1406

SchlüsselwörterArtificial Intelligence+, Data Mining, Clustering, Archival Data

© 2024 Yue Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Artificial Intelligence+, Data Mining, Clustering, Archival Data