Efficiency improvement of English online teaching system based on bagging learning flow feature selection

In the era of artificial intelligence, the traditional English teaching model can no longer meet the needs of society, and online English teaching has become the main development direction of English teaching in the future. In order to study the efficiency of English online teaching system, based on machine learning algorithms, this paper constructs an efficiency improvement model of English online teaching system. Moreover, in view of the shortcomings of current situation estimation algorithms that cannot coexist in terms of flexibility, causal interpretability and complexity, this paper proposes a biological immune algorithm framework that uses GBDT algorithm coding, which objectively and accurately shows the spread of the situation. In addition, for the problem that redundant information between features will reduce the accuracy of the framework, this paper proposes a streaming feature selection algorithm based on bagging learning. Finally, this paper designs a control experiment to analyze the performance of the model. The research results show that the model constructed in this paper is highly reliable.