Environmental audio classification has been the focus in the field of speech recognition.Random forest is a powerful machine learning classifier compared to other conventional pattern recognition techniques.In this paper,the performance of the RF classifier for environmental audio classification is explored.RF algorithm is to train multiple component tree learners and combine their predictions to enhance the accuracy of classification.The paper explains RF algorithm and its features,then employing Bagging,AdaBoost,and Random Forest for environmental audio data,the comparison and analysis of classification results are given.Optimal parameters selection,outlier detection and variable importance assessment are involved in the experiments.The experimental results show that the RF method outperforms others in the performance of environmental audio data classification.Even under the fewer number of the training examples,it provides an effective approach to guarantee the performance and generalization of classification.