A method for merging and classifying higher mathematics teaching resources based on density clustering algorithm

To enhance the recall and accuracy of resource merging classification, this study introduces a merging classification technique rooted in density clustering algorithms. Initially, we gather data pertaining to higher mathematics teaching resources. Subsequently, we convert textual sentences into word-level representations, eliminating stop words and unnecessary high-frequency vocabulary. Leveraging LDA, we extract mathematical resource features, transforming words into computer- and model-recognisable vectorised forms. Next, we calculate the density and distance between samples to categorise them into distinct groups, employing density clustering algorithms for merging and classifying teaching resources. Experimental findings reveal that our method achieves a classification recall rate of 99.6% and an accuracy of 99.9%, thereby enhancing the quality and efficacy of higher mathematics education.