Research on Automated Educational Management Information Analysis System Integrating BERT Model and Visualization Techniques

In modern education management, a large amount of unstructured text data such as teacher evaluation and student feedback needs to be processed efficiently. However, the traditional manual approach is inefficient, easily interfered by subjective factors, and difficult to meet the needs of large-scale data analysis. For this reason, we propose an automatic analysis system based on natural language processing (NLP) technology for efficient processing and in-depth analysis of educational management information. The system uses the BERT model for sentiment classification, combines with LDA topic modeling to extract key topics in the text, and intuitively displays the analysis results through data visualization tools. The experiment uses 1,000 pieces of teacher evaluation and student feedback data, assuming a sentiment distribution of 60% positive, 30% neutral, and 10% negative. The results show that the optimized BERT model achieves an F1 score of 87.2% and a recall rate of 88% on the test set, which can effectively distinguish different sentiment types. Meanwhile, the three core themes extracted from the LDA model, namely, “teaching quality, classroom management, and student feedback”, are reasonably distributed, which is in line with the reality of education management. The sentiment distribution graph and theme frequency histogram generated by the system provide valuable references for managers and significantly improve the analyzing efficiency and decision support capability.