Research on Energy Function Optimization Based on Markov Random Model

In order to prove that the existence of uncertainty has a non-negligible effect on the clustering results, the paper improves the algorithm based on energy model layout and modular clustering based on Markov random model. The experimental analysis proves the convergence of the Markov stochastic model. The algorithm can reduce the matching operation time while obtaining satisfactory energy optimization results. The results obtained not only promote some existing conclusions but also provide a certain theoretical basis for the application of the network.