Dynamical Alert of Thought and Politics Teaching Based on the Long- and Short-Term Memory Neural Network

Abstract

To strengthen and develop the thought and politics work in state-owned schools, schools must explore the theoretical system of thought and politics construction in the practice of scientific development ideas guiding school development, actively innovate the practice model and implement early warning management for thought and politics projects. This is because only by accurately analyzing the problems and causes can we seek more reasonable measures for the laws of thought and politics practice education for modern students. At present, promoting the deep integration of thought and politics teaching projects with information technology has become an important means of thought and politics teaching projects in schools. However, with the explosive growth of network data, the structure becomes more and more complex, and learners face the problem of information overload as more and more information overflows in the network environment. Precise support for students with learning disabilities is a research direction for precision thinking education, and most existing support strategies in schools include manual statistics of failed subjects, written warnings, or corrective measures through simple correlation algorithms. In this paper, we propose a dynamical alert for thought and politics teaching based on the Long-Short-Term Memory Neural Network (LSTM), which uses a powerful global optimization function to optimize the parameters of the deep LSTM neural network. The experimental results show that the average execution time of LSTM is 19.46 seconds and 8.24 seconds lower than that of SCB-DBSCAN and CFSFDP, respectively, which shows that the execution time of the LSTM algorithm is faster and more accurate. Therefore, the LSTM algorithm is feasible and effective. The LSTM-based dynamic warning of thought and politics teaching can predict students’ subject performance more accurately and has certain validity and feasibility.