As the knowledge and information society advances, among core competencies, the knowledge and information processing competency which involves collecting, analyzing, and utilizing necessary knowledge and information in the midst of a flood of information is being emphasized. The purpose of this study was to classify high school students’ knowledge and information processing competency through latent profile analysis and to explore factors affecting the classification into latent profiles with high knowledge and information processing competency by applying the random forest machine learning technique. For these purposes, data from the sixth year of the Korean Educational Longitudinal Study 2013 was used. The main conclusions are as follows. First, the types of high school students’ knowledge and information processing competency were classified into three types: advanced group, intermediate group, and beginner group. Second, it was found that student and school factors had a major influence on the classification between the advanced and other groups. Specifically, among the student factors, career maturity was found to have had a large effect. Among the school factors, it was found that classes and evaluation areas such as the meaning of subject content, inquiry and discovery-based classes, process-based evaluation, participatory classes, and competency-based classes had a major impact. Based on these results, implications for enhancing knowledge and information processing competency are discussed.