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

<p>As a precipitation and accumulation of history, archives management has gradually tended to be digitalized and informalized with the continuous updating and development of modern technology. In this paper, we first study the retrieval system and focus on the Boolean model, vector model, and probabilistic model in information retrieval technology. The matching relationship between documents and queries is detected from the document set for the user’s query, and a relevance retrieval system based on user understanding is proposed to solve the matching problem. The amount of information that needs to be retrieved is growing exponentially, and how a user perceives the information is crucial to the process. Then, in order to solve the problem of insufficient retrieval efficiency caused by the explosive growth of wisdom files, the retrieval system is creatively optimized on the basis of the ant colony algorithm, which effectively improves the efficiency of wisdom file management. The efficiency of the optimized retrieval system is verified and analyzed in an experimental simulation environment. The findings demonstrate that when the amount of archives rises, the retrieval effectiveness of the improved ant colony algorithm described in this study marginally improves, but in 10~35s. As the inventory of the Smart Archives increases, the content retrieval of the archives will become more and more frequent. This study improves retrieval efficiency and serves as a good demonstration for the construction of archival management information technology.</p>
</abstract>

As a precipitation and accumulation of history, archives management has gradually tended to be digitalized and informalized with the continuous updating and development of modern technology. In this paper, we first study the retrieval system and focus on the Boolean model, vector model, and probabilistic model in information retrieval technology. The matching relationship between documents and queries is detected from the document set for the user’s query, and a relevance retrieval system based on user understanding is proposed to solve the matching problem. The amount of information that needs to be retrieved is growing exponentially, and how a user perceives the information is crucial to the process. Then, in order to solve the problem of insufficient retrieval efficiency caused by the explosive growth of wisdom files, the retrieval system is creatively optimized on the basis of the ant colony algorithm, which effectively improves the efficiency of wisdom file management. The efficiency of the optimized retrieval system is verified and analyzed in an experimental simulation environment. The findings demonstrate that when the amount of archives rises, the retrieval effectiveness of the improved ant colony algorithm described in this study marginally improves, but in 10~35s. As the inventory of the Smart Archives increases, the content retrieval of the archives will become more and more frequent. This study improves retrieval efficiency and serves as a good demonstration for the construction of archival management information technology.

Analysis of the effect of user-understanding-based retrieval system improvement on the application of intelligent archive management

Applied Mathematics and Nonlinear Sciences

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

As a precipitation and accumulation of history, archives management has gradually tended to be digitalized and informalized with the continuous updating and...

Analysis of the effect of user-understanding-based retrieval system improvement on the application of intelligent archive management

Pubblicato online: 04 ott 2023

Pagine: -

Ricevuto: 10 dic 2022

Accettato: 14 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00469

Parole chiave
Retrieval system, Boolean model, Vector model, Probabilistic model, Ant colony algorithm, Smart archive management efficiency

© 2023 Shouzhong Wang, published by Sciendo

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

Analysis of the effect of user-understanding-based retrieval system improvement on the application of intelligent archive management

Pubblicato online: 04 ott 2023

Pagine: -

Ricevuto: 10 dic 2022

Accettato: 14 apr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00469

Parole chiaveRetrieval system, Boolean model, Vector model, Probabilistic model, Ant colony algorithm, Smart archive management efficiency

© 2023 Shouzhong Wang, published by Sciendo

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

Parole chiave
Retrieval system, Boolean model, Vector model, Probabilistic model, Ant colony algorithm, Smart archive management efficiency