Affiliations: Department of Foreign Language, Ganzhou Teachers College, Ganzhou, Jiangxi, China
Correspondence:
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Corresponding author. Huiyan Li, Department of Foreign Language, Ganzhou Teachers College, Ganzhou, Jiangxi 341000, China. E-mail: [email protected].
Abstract: When the English teaching text is regarded as the ontology, it must involve how to describe the attribute effectively. However, in the current research, the research on the automatic extraction of labels for English teaching texts is still insufficient. Intelligent English teaching has become an inevitable trend in the development of future English teaching models, so it is necessary to cooperate with intelligent text recognition technology. Based on SVM, this study applies convolutional neural network algorithm to text recognition of English teaching content, and effectively recognizes text features. After feature extraction, the original text content has been changed into data that the machine can directly identify and analyze, and semantic analysis is performed. In order to verify the performance of the algorithm, the performance of the algorithm was analyzed by example verification. It can be seen from the results that the proposed method has a certain accuracy rate and can be applied to the text recognition classification of English teaching content and can provide reference direction for related research.
Keywords: Machine learning, convolutional neural network, english teaching content, text recognition, text classification
