Extraction Method of knowledge points based on feature classification

In this paper, a knowledge point extraction method based on feature classification is proposed. First, the convolution network word segmentation model is used to segment the knowledge points and remove a large number of empty words in the text. Then, the knowledge points in the content text are classified by using the support vector machine classifier to extract the knowledge points accurately. The experimental results show that the extraction method has higher extraction precision and recall rate, and the proportion of repeated extraction data is less than 8% . It is proved that the method of knowledge point extraction based on feature classification is more effective and reliable.