Abstract：A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation Purpose This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in miningSecondthis project can effectively solve the problem of four types of rules being present in the database at the same timeThe traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendationDesignmethodologyapproach The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional AprioriFP-GrowthInverseSporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data setsFindings The method used in this project had two major advantagesFirstthe authors were able to mine four types of rules in an integrated manner without having to set interest measuresIn additionbecause it represents the relevance of mining in the networkdecision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fieldsResearch limitationsimplications The time cost of the project is still high for large data setsThe authors will solve this problem by mapping booksitemsor attributes to higher granularity to reduce the computational complexity in the futureOriginalityvalue The authors believed that knowledge of complex real-world problems can be well captured from a network perspectiveThis study can help researchers to avoid setting interest metrics and to comprehensively extract frequentrarepositiveand negative rules in an integrated mannerRetrieval and IndexingandLibrary AutomationAI mainly used in the recommended system of library in this study This study explores how to use the Markov logical network method to rebuild the exploration tasks of related related rulesespecially for library recommendation systemsThe traditional associated algorithm depends mainly on expert experienceand this method can simultaneously tap four rulesso that the knowledge in the database can be fully utilized during the decision-making process