Research on Performance Evaluation of Lagrange Mathematical Model Based on Deep Learning in High-Speed Communication Networks

In this paper, a novel algorithm model is proposed, which combines the efficient data processing capability of deep learning with the precise optimization characteristics of Lagrange mathematical model, in order to provide more accurate performance prediction in the complex and changeable network environment. The research begins with the construction of a deep learning framework, which is able to extract key features from a large amount of network communication data and learn and predict them through neural network models. Then the Lagrange mathematical model is integrated into the deep learning framework to form a comprehensive performance evaluation model. This model can evaluate the performance of communication network comprehensively while considering the multi-dimensional factors such as bandwidth, delay and packet loss rate. In order to verify the validity of the model, the corresponding system simulation platform is developed, and extensive simulation experiments are carried out in various high-speed communication network scenarios. The simulation results show that compared with traditional evaluation methods, the Lagrangian mathematical model based on deep learning has a significant improvement in prediction accuracy and response speed.