Research on Course Personalized Evaluation Model Based on Recurrent Neural Network Algorithm

Abstract

Recurrent Neural Network (RNN) is a kind of neural network with short-term memory ability. In the recurrent neural network, neurons can not only receive information from other neurons, but also receive information from themselves, forming a network structure with loops. This paper uses recurrent neural network to study the course personalized evaluation model based on intelligent optimization algorithm, and creates a set of effective evaluation modules in the course, which is conducive to the effective monitoring and quality management of the course, so as to improve the teaching quality. And it describes in detail the CNN (convolutional neural network) structure and (recurrent neural network) structure commonly used in current mainstream sentiment analysis algorithms. In the algorithm, the Bi-LSTM (long short-term memory network) structure is selected as the basic network. After innovation and improvement, it is tested for the specific data set in this paper. The construction of the Bi-LSTMA-CNNA algorithm model and the analysis of the course evaluation results provide a reference for subsequent related research.