Data mining belongs to knowledge discovery, which is the process of revealing implicit, unknown, and valuable information from a large amount of fuzzy application data. The potential information revealed by data mining can help decision makers adjust market strategies and reduce market risks. The information excavated must be real and not universally known, and it can be the discovery of a specific problem. Data mining algorithms mainly include the neural network method, decision tree method, genetic algorithm, rough set method, fuzzy set method, association rule method, and so on. Archives management, also known as archive work, is the general term for various business works, in which archives directly manage archive entities and archive information and provide utilization services. It is also the most basic part of national archives. Hospital archives are an important part of hospital management, and hospital archives are the accumulation of work experience and one of the important elements for building a modern hospital. Hospital archives are documents, work records, charts, audio recordings, videos, photos, and other types of documents, audio-visual materials, and physical materials, such as certificates, trophies, and medals obtained by hospitals, departments, and individuals. The purpose of this paper is to study the application of intelligent archives management based on data mining in hospital archives management, expecting to use the existing data mining technology to improve the current hospital archives management. This paper investigates the age and educational background of hospital archives management workers and explores the relationship between them and the quality of archives management. Based on the decision number algorithm, on the basis of the database, the hospital data is classified and analyzed, and the hospital file data is classified and processed through the decision number algorithm to improve the system data processing capability. The experimental results of this paper show that among the staff working in the archives management department of the hospital, 20-to-30-year-olds account for 46.2% of the total group. According to the data, the staff in the archives management department of the hospital also tends to be younger. Among the staff under the age of 30, the file pass rate was 98.3% and the failure rate was 1.7%. Among the staff over 50 years old, the file pass rate was 99.9% and the failure rate was 0.1%. According to the data, the job is related to the experience of the employee.

Due to the recent advances of Internet and information technologies, massive quantity of archive data gets generated and it becomes difficult to handle it using conventional techniques. Archive management is the field of management related to the maintenance and utilization of archives, once they have been sent from the client to the repository. The drastic increase in the size of archive data necessitates effective storage schemes, which can be accomplished by the use of data compression approaches. Generally, data compression techniques are used for reducing the count of data being saved from a system or network without compromising the data quality. With this motivation, this study designs an effective archive storage system with a compression approach for network management (EASS-CANM). The major intention of the EASS-CANM technique is to archive the textual and image data effectively in its compact form in order to reduce the storage area. In the context of archive management, the EASS-CANM technique might be considered a successful instrument. The proposed EASS-CANM technique involves a two-stage process: textual data compression and image compression. At the initial stage, neighborhood indexing sequence (NIS) with the Prediction by Partial Matching (PPM) technique was applied for textual data compression. Secondly, fruit fly optimization (FFO) with modified Haar wavelet (MHW) is used for effective image compression where the optimal threshold selection process takes place utilizing the FFO technique. We improved the Haar wavelet filtering process in order to preserve higher image quality and clarity (MHW). With the introduction of MHW, a new transformation is made possible, allowing for improved compression outcomes as well as improved PSNR and CR values. In order to demonstrate the improved outcomes of the EASS-CANM approach, a series of simulations are performed utilizing a benchmark dataset. The experimental results reported the supremacy of the EASS-CANM technique on existing approaches. The benchmark dataset is used to conduct a wide range of studies to see whether the EASS-CANM approach improves archival efficacy. According to the full comparative result analysis, the EASS-CANM strategy is more effective than existing approaches in terms of numerous evaluation criteria. Therefore, the EASS-CANM technique can be used effectively in the administration of archives.