A Prediction Method for Evaluating the Quality of Chinese Teacher Education Based on CNN-LSTM

With the continuous and vigorous development of China's education industry, the evaluation of teacher education quality, as a core link to ensure educational level and improve educational quality, has attracted widespread attention. In response to this issue, this article proposes a CNN-LSTM model for predicting and classifying the evaluation of teacher education quality. Firstly, by constructing a dictionary on the preprocessed text, words are constructed as word embedding matrices to achieve encoding and representation of text information. Then, convolutional neural networks (CNN) and pooling layers are used to extract local hidden features from the text. Next, input the extracted matrix into the Long Short Term Memory Network (LSTM). Finally, the output vector of LSTM is passed into the fully connected layer for prediction classification, achieving prediction of education quality evaluation. Through experimental comparison and verification, this method has achieved good results in improving the accuracy and efficiency of educational quality evaluation and prediction.