Classification and Application of Teaching Evaluation Text Based on CNN and Stacked Bidirectional LSTM

To thoroughly mine the vertical information contained in the unstructured data (text evaluation) in the teaching evaluation data and to make sure that the effective content contained in it can be used and the teaching quality will be improved completely, considering that the traditional text sentiment analysis method cannot adapt to the complexity and change. The language context has certain limitations. Taking objective data from the Department of Computing of Qinghai University as an example, this paper proposes two efficient classification methods, Convolutional Neural Network (CNN) and Stacked Bidirectional Long Short Term Memory (LSTM), and performs sentiment value calculation, descriptive analysis, and characteristic analysis based on classification, and further Excavated the essential information contained in the text teaching evaluation. The experimental results show that the average classification accuracy of the proposed method can reach 98%, which effectively solves the problem of text classification and application for teaching evaluation. This method has been applied in the actual teaching improvement link of the Computer Department of Qinghai University, and its effectiveness further demonstrates the advanced nature of the method and provides an important reference for the fundamental improvement of the teaching level.