Ultrasonic detection is a common method of steel defect detection. The classification model established by machine learning algorithm can realize effective defect identification. Neural network is the most commonly used algorithm at present, but it has the problem of complex model structure and large amount of training data. In this paper, an ultrasonic defect recognition method based on random forest is proposed, which can realize intelligent and accurate identification of defect types to solve the problems of complex model structure and large training data requirements. Firstly, ultrasonic detection experiments were carried out for defects of different shapes, sizes and depths in the specimen. Based on the experimental data, an ultrasonic defect recognition model was established using random forest algorithm. Then, the defect recognition effect of the model is analyzed, and compared with support vector machine, K-nearest neighbor classification algorithm, AdaBoosting algorithm and convolutional neural network. Then the defect identification verification experiment is carried out with the verification specimen to further verify the validity of the established defect identification model. The results show that the proposed method has the highest accuracy compared with other algorithms, and the accuracy of defect classification reaches 94.6% in the verification experiment.