Evaluation of English Subjective Questions Based on Deep Neural Networks

In the background of artificial intelligence (AI) era, deep neural networks (DNNs) also have a far-reaching impact on all walks of life. For the field of education, the integration of deep neural network technology and language teaching is more in-depth. Targeting at the problems of high costs and low efficiency of manual evaluation, this paper aims to study the evaluation of the English subjective questions by using deep neural networks. Firstly, the surface features of the English character information, including English antisense, negative information, semantic features, word frequency, sentences length, and words order, are combined; and the calculation method of the English sentence similarity based on multifeature fusion is established through the analytic hierarchy process (AHP). Secondly, the English text recognition is composed of long and short memory lost by aggregation cross entropy. The introduction of aggregation cross entropy can effectively improve the accuracy of text recognition. Finally, the evaluation model based on deep neural network is used to evaluate the English subjective questions. The experimental results, in terms of accuracy, recall rate, and effectiveness of the automatic recognition, show that the proposed method has high accuracy and can effectively improve the quality of the English teaching and realize a personalized teaching approach.