Data mining and neural network modeling for teaching and learning in vocational education: promoting innovation in academic management and teaching reforms

In order to provide students with dynamic and personalized academic early warning services, and at the same time provide university administrators with data-supported decision-making, this paper uses data cleaning, transformation and normalization methods to preprocess the data to normalize it into an analyzable dataset according to the characteristics of educational data. For this dataset, an academic early warning model (TabNet) combined with KNN neural network algorithm is proposed to train the early warning data and analyze and compare its performance with the BP neural network algorithm. Finally, it provides ideas for the construction of intelligent classrooms for vocational education teaching. The results show that the average accuracy and recall of the classifier on the test set are high, 93.11% and 74.35%, respectively; the classification of positive examples on the training set is not precise enough, and its average precision and recall are 81.36% and 47.07%, respectively. The recall, precision and F1 mean of the support vector machine on the training set are 87.36%, 91.14% and 81.57%, which are close to the test set. The Loss curve of the TabNet algorithm has a better generalization performance on both the test and training sets, with a low chance of overfitting and minimal differences. In addition, the TabNet neural network algorithm has higher accuracy on the ROC curve and is more valuable when using the classification results.