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

<p>In the background of the era of big data, the information management system of engineering archives has become more comprehensive and perfect because of the application of information technology. The application of deep learning model makes the management of engineering archives more systematic, scientific and standardized, which greatly improves the quality and efficiency of engineering archives management. The progress of society and the development of the times have put forward higher requirements for digital storage technology. This paper combines the characteristics of the new technological era, analyses the characteristics of traditional information management in the context of data processing, artificial intelligence, deep learning and other data, proposes a method for developing and managing web archives based on Bootstrapping technology, introduces an information meta-evaluation mechanism to improve the quality of mining, and uses a long and short-term memory model to extract multi-type fine-grained archival information elements in the corpus. Finally, the Alex network was established to manage the archives in a categorised manner. The experimental results show that the query results of the proposed method for the target archives are 100% accurate, and the query time for individual archives is basically within 5s, which has good management effect.</p>
</abstract>

In the background of the era of big data, the information management system of engineering archives has become more comprehensive and perfect because of the application of information technology. The application of deep learning model makes the management of engineering archives more systematic, scientific and standardized, which greatly improves the quality and efficiency of engineering archives management. The progress of society and the development of the times have put forward higher requirements for digital storage technology. This paper combines the characteristics of the new technological era, analyses the characteristics of traditional information management in the context of data processing, artificial intelligence, deep learning and other data, proposes a method for developing and managing web archives based on Bootstrapping technology, introduces an information meta-evaluation mechanism to improve the quality of mining, and uses a long and short-term memory model to extract multi-type fine-grained archival information elements in the corpus. Finally, the Alex network was established to manage the archives in a categorised manner. The experimental results show that the query results of the proposed method for the target archives are 100% accurate, and the query time for individual archives is basically within 5s, which has good management effect.

Engineering archive management model based on big data analysis and deep learning model

Applied Mathematics and Nonlinear Sciences

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

In the background of the era of big data, the information management system of engineering archives has become more comprehensive and perfect because of the...

Engineering archive management model based on big data analysis and deep learning model

Online veröffentlicht: 03. Juni 2023

Seitenbereich: -

Eingereicht: 25. Juli 2022

Akzeptiert: 26. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00212

Schlüsselwörter
big data context, deep learning, archive development management, bootstrapping techniques, Alex network

© 2023 Shuiting Du et al., published by Sciendo

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

Engineering archive management model based on big data analysis and deep learning model

Online veröffentlicht: 03. Juni 2023

Seitenbereich: -

Eingereicht: 25. Juli 2022

Akzeptiert: 26. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00212

Schlüsselwörterbig data context, deep learning, archive development management, bootstrapping techniques, Alex network

© 2023 Shuiting Du et al., published by Sciendo

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

Schlüsselwörter
big data context, deep learning, archive development management, bootstrapping techniques, Alex network