The Mixture-of-Experts (MoE) architecture has emerged as a promising paradigm for scaling large language models (LLMs) by activating only a sparse subset of experts per input. However, its massive parameter size remains a major obstacle to efficient deployment. Existing pruning methods often ignore two key aspects: the intricate structural dependencies among experts and the heterogeneous importance of different layers. To tackle these issues, we propose C-GNN-PRUNE, a unified and structure-aware compression framework tailored for MoE models. Our method introduces an EntropyGuided Allocation Module that dynamically assigns pruning budgets by leveraging expert activation entropy, enabling adaptive handling of inter-layer heterogeneity. To preserve structural collaboration patterns, we construct an expert interaction graph that fuses functional similarity and routing behavior, and employ a GNN-Based Embedding Module to learn structure-aware expert representations. These embeddings, along with co-activation patterns, are fed into a Community Detection Module to identify expert clusters for structured pruning. Finally, an Activation-Aware Selection Module retains the most critical experts in each community, balancing sparsity and expressiveness. Experiments on multiple open-source MoE models demonstrate that C-GNN-PRUNE consistently outperforms prior methods under various pruning ratios, achieving better trade-offs between compression and accuracy. This framework provides a modular and effective solution for structure-preserving compression of large-scale MoE models.

The data that records road traffic accidents often hides important information. If we can analyze the main factors that affect the severity of the accident in the data, it has very good practical significance for avoiding the occurrence of major traffic accidents. In this paper, a method called CFS-BAS-BP is proposed to realize the identification of potential traffic accident risk factors based on combinatorial feature selection and bionic neural network. This method combines the advantages of Information Gain, Chi-square test and Random Forest in feature selection to screen the important features affecting traffic accidents to the greatest extent, and filter out irrelevant and redundant features in the data. As a result, 14 features that influence the occurrence and severity of traffic accidents are selected from 25 features in the dataset At the same time, the back propagation neural network optimized by the beetle antennae search algorithm is used to analyze the quantitative relationship between traffic data features and traffic accident risk level, so as to identify the main risk factors affecting the severity of traffic accidents in different scenarios. Through a large number of experiments on the real traffic accident data set STATS19, we prove that CFS-BAS-BP model has a significant effect on the selection of accident-related features, and its accuracy rate of identifying the risk factors affecting the severity of traffic accidents reaches 86.1%.