In the digital information age, data mining technology is becoming more widely used in libraries for its useful impact. In the context of big data, how to efficiently mine big data, extract features, and provide users with high-quality personalized service is one of the important issues that needs to be solved in the current university library big data application. Brain computing is a kind of comprehensive processing behavior of the human brain simulated by the computer, which can comprehensively analyze a variety of information and play a very good guiding role in processing library service behavior. This paper briefly introduces the related concepts and algorithms of data mining technology and deeply studies the classical algorithm of association rules, namely, Apriori algorithm, which analyzes the necessity and feasibility of applying data mining technology to university library management. The design idea and functional goal of the college book intelligent recommendation system are based on the decision tree method and association rule analysis method. Through the application research of data mining technology in the personalized service of the university library, combined with the actual work, this paper proposes data mining of association rules in the university library system. The research further elaborates on the system architecture, data processing, mining implementation algorithms, and application of mining results. The experimental results of the research have certain significance for the university library to explore personalized services, provide book recommendation services, and make corresponding decisions to optimize the library’s collection layout.

中文翻译：

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基于数据挖掘的高校图书馆服务模式研究

在数字信息时代，数据挖掘技术因其有益的影响而在图书馆中得到越来越广泛的使用。在大数据的背景下，如何有效地挖掘大数据，提取特征并为用户提供高质量的个性化服务是当前大学图书馆大数据应用中需要解决的重要问题之一。脑计算是计算机模拟的一种人脑的综合处理行为，它可以对各种信息进行综合分析，在处理图书馆服务行为中起很好的指导作用。本文简要介绍了数据挖掘技术的相关概念和算法，并深入研究了关联规则的经典算法，即Apriori算法，分析了将数据挖掘技术应用于高校图书馆管理的必要性和可行性。高校图书智能推荐系统的设计思想和功能目标是基于决策树法和关联规则分析法的。通过数据挖掘技术在高校图书馆个性化服务中的应用研究，结合实际工作，提出高校图书馆系统关联规则的数据挖掘。该研究进一步阐述了系统架构，数据处理，挖掘实现算法以及挖掘结果的应用。研究的实验结果对于大学图书馆探索个性化服务，提供图书推荐服务，