As an important component of the human digestive system, the oral cavity plays a crucial role in the occurrence of many diseases. In recent years, numerous studies have confirmed that oral diseases such as periodontal disease, dental caries, and oral cancer are closely related to the oral microbiota. This work focus on the classification the coding regions and the non-coding regions in several oral microorganisms, including acinetobacter baumannii, lactobacillus casei, lactobacillus fermentum, and streptococcus mutans. The kmer and RCkmer features have been employed. In order to compare the performances of this methods, several SOTA methods, including linear discriminant, support vector machine (SVM), random forest, and adaboost, have been utilized in this work. The performances demonstrated that the cascade forest can achieve available results than other methods.