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In this paper, the evaluation index system of information literacy of trainees is determined by expert Delphi method. Questionnaires are conducted according to the problems in the second-level indicators, and the results are analyzed by second-order cluster analysis. The information literacy level of trainees is divided into groups, and the existing problems and shortcomings are pointed out, which provides effective guidance for further information literacy education. Introduction With the arrival of the era of big data, how to quickly extract key information from massive data and bring value to enterprises and individuals is the focus of attention. At present, many traditional data analysis methods can also be used in large data analysis, such as clustering analysis, factor analysis, correlation analysis and regression analysis. At present, many traditional data analysis methods can also be used in large data analysis, such as clustering analysis, factor analysis, correlation analysis and regression analysis. Information literacy is the essential basic quality of new military talents, the need of lifelong learning and the motive force of innovation. The evaluation of information literacy of cadets in military academies is the research direction of the further development of information literacy theory. By referring to the information literacy standards and indicators formulated by scholars at home and abroad, this paper tries to formulate an “information literacy evaluation index system” for our cadets, and based on this evaluation body. The department evaluates and analyses the current situation of trainees' information literacy. Cluster Analysis Cluster analysis is a statistical method for partitioning objects, referring to the classification of objects or things with some similar characteristics. The purpose of cluster analysis is to distinguish the unknown and similar things in some characteristics, and divide the samples into several categories or groups according to these characteristics, so that the things in the same category have a high degree of homogeneity, while the different things have a high degree of heterogeneity. The second-order clustering analysis in clustering analysis is exploratory analysis. It is mainly applied to pattern classification in cross-domain of data mining and multi-metadata statistics. Using distance measure, it is assumed that the variables of clustering model are independent variables, and that is continuous variables are assumed to be normal distribution, and classified variables are polynomials. Empirical internal test method is used to stabilize the interference of independent variable hypothesis and distribution hypothesis. Second-order clustering analysis can produce different clustering discriminant information, such as the clustering frequency of the final clustering, descriptive statistics of the final clustering, and pie chart, bar chart and importance map of the variables of the clustering frequency. Second-order clustering analysis has the following characteristics: firstly, it can deal with classification variables and continuity variables, and synthetically process data of multi-normal distribution by assuming that variables are independent variables; secondly, it can automatically

The Information Literacy Assessment of Trainees Based on Cluster Analysis