This article explores the application of digital information technology in music education, especially in teaching traditional music. Taking the intersection of conventional music culture and applied teaching practice, the article utilizes advanced algorithmic techniques - Support Vector Machines and Apriori Association Rule Algorithm - to construct an innovative model of emotional cognition and stylistic categorization of traditional music. This model integrates standard music emotion cognition and style classification into modern informationized music teaching. To verify the model’s effectiveness, the researchers selected 500 pieces of traditional Chinese music as samples, trained and tested using the constructed model, and compared it with other algorithmic models. The test results show that the model outperforms BP neural networks and Linear neural networks regarding emotion perception. Specifically, the model shows efficient performance with mean square error (MSE) and correlation coefficient (CC) averages of 0.0099 and 90.50% on the test set, respectively. In addition, the model has the shortest processing time and is lower than the IM-Apriori algorithm regarding space consumption, showing its efficiency and resource-saving advantages. The model’s accuracy ranges from 75.23% to 90.06% in classifying traditional music. In summary, the model proposed in this paper improves the accuracy and efficiency of emotional cognition and style classification of conventional music and provides essential technical support for effectively integrating traditional music into modern informative music teaching.