As the most basic element in English learning, vocabulary has always been the focus of teaching in college English classes, but the teaching effect is often unsatisfactory. In this paper, the genetic algorithm fitness function design part is integrated with the K-medoids algorithm to form K-GA-medoids, and secondly, it is combined with KNN to form an algorithmic framework for English vocabulary classification. In the classification process, clustering and classification steps are taken to realize the reduction of the training set samples and thus reduce the computational overhead. The experiments show that K-GA-medoids have significantly improved the clustering effect compared with traditional K-medoids, and the combination of K-GA-medoids and KNNs has effectively improved the efficiency of English vocabulary classification compared with the traditional KNN algorithm, while ensuring the classification accuracy. We found that students in college English course consider word memorization as a difficult learning task, and the traditional vocabulary teaching methods are not very effective, and the knowledge of etymology is often little known and rarely covered in classroom lectures. Therefore, the article explores new ideas and strategies for teaching vocabulary in college English from the perspective of etymology.

Online English teaching resources have recently surged, highlighting the exigency for efficient organization and categorization. This manuscript introduces an innovative strategy to classify university-level English teaching resources, employing a sophisticated density clustering algorithm. Initially, student discourse was mined within a teaching platform comment section, and in-depth textual analysis was conducted. Subsequently, the term frequency-inverse document frequency (TF–IDF) feature extraction algorithm was enhanced, while emotive attributes were seamlessly integrated into the textual manifestation layer during the classification procedure. This enabled the distribution of topics and emotions to be acquired for each comment, facilitating subsequent analyses of emotion feature extraction and model training. An improved weight calculation was designed based on TF–IDF to evaluate the importance of feature items for each corpus file. The simulation results demonstrate the proposed scheme's effectiveness. The algorithm facilitates faster scholarly access to educational resource information and effectively classifies data for high research adaptability.

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