Numerical Analysis and Optimization of Feature Extraction-Oriented English Reading Corpus

English is a universal language in the world. It has become the consensus of society as a subject of education in primary and secondary schools and even universities. Therefore, how to improve English reading ability has also become a focus area of school education and students. The current research on English reading is mainly based on the sense of reading questions, reading patterns, answering skills, etc. and lacks the analysis of English reading corpus. In view of this, this paper used a self-built English reading corpus, adopts the feature extraction method, and combines the convolutional neural network (CNN) to build a model to carry out numerical analysis on the self-built English reading corpus, optimized the model, and compared and analyzed the results obtained. The optimal dropout rate and iteration times were obtained by updating experimental parameters. In order to make the experimental results more convincing, the W2V-SVM and W2V-CNN models that combine different feature extraction and classification methods are designed. Compared with the optimized CNN model, the accuracy rate, recall rate, and F1 value of the optimized CNN model were 89.81%, 92.39%, and 92.8%, respectively. The accuracy, recall, and F1 value of the W2V-SVM model are 81.31%, 82.09%, and 81.25%, respectively. The accuracy, recall, and F1 value of the W2V-CNN model are 85.24%, 84.98%, and 85.12%, respectively. It shows that the optimized CNN feature classification model has better feature classification effect on the self-built English reading corpus. The experimental results meet the expected value.