This article presents a novel mathematical and bioinformatics framework for constructing molecular ecological networks (MENs) using Random Matrix Theory (RMT)-based methods. The approach automatically defines network structures and is robust to noise, providing effective solutions for analyzing high-throughput metagenomics data. The framework was applied to determine the network structure of microbial communities subjected to long-term experimental warming using 16S rRNA gene pyrosequencing data. The resulting MENs exhibited topological features of scale-free, small-world, and modularity, consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented module profiles well. Environmental traits such as temperature and soil pH were found to be important in determining network interactions. A comprehensive Molecular Ecological Network Analysis Pipeline (MENAP) was developed, integrating all methods and statistical tools for open-access use. The RMT-based approach provides powerful tools for elucidating network interactions in microbial communities and their responses to environmental changes, which are fundamental for microbial ecology and environmental microbiology. The study highlights the importance of network interactions in microbial communities, showing that they are more critical than species richness and abundance for ecosystem functioning. The RMT-based method automatically determines thresholds for network construction and is robust to noise, making it suitable for analyzing complex microbial community data. The study also demonstrates the application of MENs in understanding how environmental changes affect microbial community interactions, with results showing that different submodules respond differently to environmental changes. The open-access pipeline allows for the analysis of microbial community networks, providing insights into the functional roles of organisms in biogeochemical processes and the stability of microbial communities. The study concludes that the RMT-based molecular ecological network analysis is a powerful tool for understanding microbial community interactions and their responses to environmental changes.This article presents a novel mathematical and bioinformatics framework for constructing molecular ecological networks (MENs) using Random Matrix Theory (RMT)-based methods. The approach automatically defines network structures and is robust to noise, providing effective solutions for analyzing high-throughput metagenomics data. The framework was applied to determine the network structure of microbial communities subjected to long-term experimental warming using 16S rRNA gene pyrosequencing data. The resulting MENs exhibited topological features of scale-free, small-world, and modularity, consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented module profiles well. Environmental traits such as temperature and soil pH were found to be important in determining network interactions. A comprehensive Molecular Ecological Network Analysis Pipeline (MENAP) was developed, integrating all methods and statistical tools for open-access use. The RMT-based approach provides powerful tools for elucidating network interactions in microbial communities and their responses to environmental changes, which are fundamental for microbial ecology and environmental microbiology. The study highlights the importance of network interactions in microbial communities, showing that they are more critical than species richness and abundance for ecosystem functioning. The RMT-based method automatically determines thresholds for network construction and is robust to noise, making it suitable for analyzing complex microbial community data. The study also demonstrates the application of MENs in understanding how environmental changes affect microbial community interactions, with results showing that different submodules respond differently to environmental changes. The open-access pipeline allows for the analysis of microbial community networks, providing insights into the functional roles of organisms in biogeochemical processes and the stability of microbial communities. The study concludes that the RMT-based molecular ecological network analysis is a powerful tool for understanding microbial community interactions and their responses to environmental changes.