In this paper, based on the knowledge sharing in the college English teaching efficiency is low, and the existing network auxiliary teaching platform in the “information explosion” of potential crisis, with the demand of the teachers and students of high level of knowledge management, make full use of computer network technology, network database technology, on the basis of knowledge discovery technology. This paper constructs a platform model of college English teaching resource system based on knowledge base management platform, and expounds and analyzes the main function modules, implementation technologies, and attention matters in operation and management of this platform. This platform can provide rich knowledge base resources and convenient interactive communication, and improve the level of knowledge management in English teaching. Constantly improve the quality of teaching. Of great significance.

Research on the metrological characteristics of linguistic quantitative characteristics (LQCs) based on corpus and metrological linguistic methods has gained wide attention in artificial and online machine translations. Although a support vector machine (SVM) is one of the most widely used machine learning (ML) algorithms in the field of text analysis, its application in the study of translation style is rare. This study compares the translation styles of Pride and Prejudice with ML using different linguistic measurement features. Firstly, the language measurement features of three translations are obtained with the information gain algorithm. Specifically, the corpus can be achieved through human-machine interaction (HCI), i.e., computers can look, hear, touch, smell, taste, and speak using sensors such as cameras and mathematical algorithms. Then a text classifier, i.e., an SVM, is constructed on the basis of these features to automatically classify the translated texts of the three translations. Finally, the validity of the classifier is verified by the tenfold cross-validation method. It is proved that the SVM algorithm has high classification accuracy and a strong predictive function, which is helpful for judging or predicting the translation or translator's style. Compared with the traditional method, this classification method based on an SVM saves time and effort, the process can be repeated, and the result is accurate and reliable.

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Now, the application of intelligent technologies such as machine learning and deep learning in natural language processing has achieved good results. This article studies the integration of emotion analysis in English module teaching of natural language processing. Vocabulary is a very important part in English teaching. Learning vocabulary well can improve students’ reading ability. However, in the process of students’ learning, vocabulary is the most basic and difficult to learn. Poor vocabulary learning and insufficient accumulation will restrict students’ reading ability. Improving vocabulary teaching mode and learning methods can stimulate students’ interest in learning and effectively improve their reading ability. In the third part of the article, the neural network language model and statistical model are used to analyze the key technologies of natural language processing, and then the Naive Bayes algorithm and support vector machine model algorithm are used to optimize the data. Finally, two classes are selected for comparative experiment, then, by integrating emotional teaching into students’ classroom and analyzing students’ interest, the conclusion is that integrating emotional teaching in teaching can effectively improve students’ academic achievements, and at the same time, integrating emotional teaching in teaching can also stimulate students’ enthusiasm for learning English and effectively change students’ learning attitude.