Performance Evaluation Method of Library Knowledge Management Based on Data Mining

Abstract

With the development of information technology and computer technology, the work of the library is becoming more and more digitized and networked. This article mainly studies the performance evaluation method of library knowledge management based on data mining. This paper uses attribute-oriented induction algorithm to mine generalized features. First, scan the entire data set to obtain different values of all attributes. Statistics, classification, and analysis of historical records help to understand the usage of books and periodicals and perform predictive analysis. This article uses statistical weighted weight calculation formula to calculate the library knowledge management ability evaluation index weight. The evaluation of the knowledge management ability of the library mainly adopts the questionnaire survey method, and the knowledge management status of the library is deeply understood in the form of interviews on the spot, and the obtained evaluation data and materials of the knowledge management ability of the library are organized and statistics. In order to prevent the model from remembering the patterns of the training set too deeply, to make the model more general, and to adapt to unknown data well, we use the test set to rest the model. The part of the data set that has not been used in the process of modeling and testing correction can be used to estimate the effect of the model, or to compare the effect of the model. For expert value, the value of the office is 1.02, which is approximately equal to 1, which means that the value and cost of the office are basically equal in the knowledge service of the library. The results show that the combination of knowledge management and university library management will help to form a systematic theoretical framework and behavioral model framework in the research of knowledge management models and strategies in university libraries.