Genetic algorithm and support vector machine application in English text classification for intelligent teaching

With the rapid development of computer technology, the amount of information data has exponentially increased, and the complexity of English text has become more challenging. However, there is still a significant amount of irrelevant and harmful information in English text data, which makes it difficult to effectively and efficiently utilize this data. As a result, developing effective methods for text classification has become a hot research topic in various industries and fields. This study also proposed a combination of the GA-SVM model and GA-FCM (genetic algorithm-fuzzy c-means) model. By using the GA-FCM model in conjunction with the GA-SVM model, the progressive clustering model can be developed, which can effectively improve the efficiency and accuracy of text classification results. Experimental results have shown that the proposed GA-SVM and GA-FCM models can significantly improve the efficiency and accuracy of text classification. The progressive clustering model based on these models can effectively filter out irrelevant and harmful information in English text data and accurately classify the text data into different categories. This has significant implications for various industries and fields, such as finance, healthcare, and social media, where effective text classification can enhance decision-making processes and improve overall performance.