Branding Strategies for Education Using NLP and Knowledge Based Systems

Knowledge-based systems can provide educational institutions with tailored branding strategies based on their data-driven insights, so it is necessary to integrate advanced computational methods. People are accustomed to using informal and dynamic language on the internet to express their preferences and expectations. In order to obtain actionable insights from large-scale textual data, it is necessary to employ natural language processing techniques to extract meaningful patterns and trends. Therefore, the study introduced TF-IDF analysis and Topic Modeling for textual data analysis. At the same time, the study introduced TF-IDF analysis and Topic Modeling for identifying key branding themes and introduced a knowledge-based system combining rule-based decision-making models and ontological frameworks based on branding domain knowledge, thereby constructing a comprehensive branding framework. The innovation of the research lies in the synergistic combination of traditional frequency-based analysis and coherence-driven topic modeling feature weight calculation methods with knowledge-based systems to formulate adaptive branding strategies, thereby improving the efficiency and precision of branding methodologies. The outcomes indicated that the accuracy of the classification model combining Topic Modeling and TF-IDF based on educational branding datasets was 94%, the coherence score was as high as 0.724, and the term frequency recall was 89%. Meanwhile, the classification error rate of the model was only 6%. In addition, the proposed branding system, which adopted a knowledge-driven framework, had an average of 2,500 daily visits per person, with an effective browsing time of 12 minutes per person, and a daily browsing page count of 15 pages per person. This indicates that the knowledge-based branding framework has strong practical application effects and provides reliable technical support for current research in the field of educational branding.