Research on an Environmental Category Inference Model for Microbiome Data Based on Neural Networks

This paper proposes a neural networkbased computational model that infers the environmental category of a sample from microbiome data by analyzing the composition and abundance features of microbial communities. We adopt a two-layer fully connected neural network architecture and perform data standardization, feature engineering, and model training on microbial abundance data. On a multi-class environmental dataset, the model achieves strong performance with a micro-averaged AUC of 0.9675 and a macro-averaged AUC of 0.9701. The experimental results indicate that the model effectively captures distinctive community patterns across environments, providing a quantitative analytical tool for environmental microbiology, niche analysis, and environmental monitoring. Biological interpretation of the model outputs further reveals potential associations between specific microbial genera and environmental categories, laying the groundwork for deeper investigation into the mechanisms of environmental adaptation in microbial communities.