Machine indexing Provides compatibility between classification codes and indexing terms, extracted expressions and words automatically from a Compiled thesaurus.. In designing an auto-indexing system, computer completely replaces humans. The purpose of this research was to identifying and extracting keywords and the subject trends of articles in the field of information retrieval and the subject’s specificity of the author of each article by using the text mining and categorizing (classifying) with the help of concurrence vocabularies. The method of this research is applied and based on the CRISP model of data mining and text mining algorithms are used. The research population consists of 313 articles in the field of information retrieval indexed in the Normmags database. After normalizing the text of the articles by the Virastyar software, and after text mining of the articles by version 7.1 of the RapidMiner software, the keywords were extracted by calculating their weight and were analyzed using two classical classification algorithms consisting of KNN and Naïve Bayse.In this study, the computer automatically indexed the readable machine text by using the frequency of the words with the help of the text mining tools of RapidMiner software. For this purpose, we use N-gram operators and calculate the weight of the words according to TF-IDF method. Terms and key concepts and subject and specialization of author of each article are extracted in the form of 16 categories. Finally, the superiority of the KNN model in categorization of the core subjects of the papers, this study is proving to be 85% more accurate than the Naïve bayse model. Finding the results of calculating the accuracy of the models indicate the acceptable performance of the RapidMiner software in machine indexing of texts. Indexing texts by using this method can help improve the results of information retrieval and prevent false dropping of information in databases.