Security protection strategy of power terminal based on deep learning

In order to solve the problem of security vulnerabilities in embedded power terminals, this paper studies the application of long short-term memory neural network to the security protection of power terminals, and establishes a security protection model. According to the characteristic that the protection model lacks negative samples for training in the training process, the study uses the characteristics of long short-term memory neural network to improve the model, and compares and analyzes it with the same type of model in practical applications. The results show that the model designed by the research has a model accuracy of 0.99 in the original dataset and a model accuracy of 0.98 in the mixed dataset, and the model performance data is the best among the same type of models. It can be seen that the model of research design has sufficient practicability and can provide a new idea for related fields.