Application of Deep Learning and Computer Data Mining Technology in Electronic Information Engineering Management

Abstract

This article studies the application of deep learning and computer data mining technology in electronic information engineering management to meet the library’s demand for larger collection space and alleviate the management pressure of book preservation, borrowing, and return. This article also utilizes the general information mining function to further improve the information retrieval function. The conclusion of this article is as follows: Based on the traditional April algorithm, an improved address based April algorithm is proposed. The improved Apriori algorithm can reduce the final number of permanent data packets and save about 70% of time. For the minimum supported changes, the improved Apriori algorithm has lower execution time and approximately 60% time reduction. This article develops a data mining algorithm that is more suitable for electronic library management information systems based on existing data mining technologies. Has clear theoretical and practical significance.