Research on the Algorithm of Student Behavior Data Mining in Colleges and Universities Based on Deep Learning

This paper proposes a student behavior prediction algorithm based on MLP and adaptive learning rate optimization. The algorithm combines the powerful nonlinear modeling ability of multilayer perceptron and the advantages of adaptive learning rate optimization algorithm, and can effectively process the complex features in student behavior data and improve the prediction accuracy. A student behavior dataset from an online learning platform was used, including data in multiple dimensions such as learning time, number of interactions, and grades. Experiments show that this algorithm is effective in predicting students' behavior. The predicted precision of the algorithm is 95.6 %, the recall rate is 92.3 %, and the FR is 94.0 %. Compared with traditional machine learning algorithms (SVM, DT, etc.), the precision is increased by 6 percent, and the training speed is faster, about 25 %. In addition, the algorithm showed strong robustness and generalization ability when processing high-dimensional features and sparse data. This study provides new ideas for the analysis of student behavior data, especially in student grade prediction, behavior analysis and personalized learning recommendation. It has important application value.