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In this study, we have established a feature extraction process for the classification of unknown genres of Turkish texts by using Turkish morphology. The proposed method considers the features as the word stems. The fact that the number of the features exceeds the practical computing limits each document represented by a number of features as in the document classes. Each word stem in a document analyzed in different classes and the sum of the usage frequency in the document classes given the feature value of that document. To speed up the process of extracting usage frequencies of word stems and analyzing it in different document classes Trie tree structure has been used. In this study, we have selected five different classes which are economy, healthy, magazine, sports and politics. The performance of the established method has been compared the bag of words approach by using naive Bayes, support vector machine, K-nearest neighbor, C 4.5 and random forest. The best performance achieved is 96.25% which has been observed using the naive Bayes with our new feature vectors.