Objective We compared the rhizosphere microbial interaction network structure and keystone taxon identification arising from distinct network construction algorithms, aiming to clarify the characteristics and advantages of each algorithm in inferring microbial interactions and identifying keystone taxa, thereby providing a theoretical basis for methodological selection. Methods Taking the rhizosphere microbial community of Camphora migao (a rare plant) as the model system, we constructed molecular ecological networks with three mainstream algorithms: sparse correlations for compositional data (SparCC), random matrix theory (RMT), and co-occurrence network (CoNet). We comprehensively compared network structural features and keystone taxon identification across algorithms by integrating PICRUSt2 functional prediction with keystone taxa-environmental factor correlation analysis. Results Network construction algorithms significantly influenced the topological properties of networks. SparCC generated highly modular networks (relative modularity index, RM=1.31) with distinct interaction segregation (edge connectivity=0). RMT produced a single-module structure (RM=0.78) and homogeneous connectivity (closeness centralization index=0.22). Integration of 26.0% negative correlations in CoNet reduced modularity (RM=0.95), increased network diameter (33.22 steps), and decreased robustness. Keystone taxon identification was method-dependent. Specifically, CoNet, SparCC, and RMT identified 224.00, 44.00, and 19.00 keystone taxa, respectively, with<9.2% cross-method overlap. Rhizobiales and Acidobacteriales were consistently identified as core keystone taxa by all methods, demonstrating cross-algorithm stability. The correlation analysis with environmental factors confirmed that these shared taxa significantly correlated with β-glucosidase activity, validating their role in cellulose degradation and highlighting methodological consistency in identifying key ecological processes. Conclusion The three algorithms exhibited complementary strengths: CoNet resolved complex competitive interactions; SparCC reliably assessed functional stability; RMT uncovered core functional modules. The correlation analysis with environmental factors validated the cellulose degradation function of keystone taxa, with high cross-method consistency in core ecological process identification. Our work provides a theoretical foundation for elucidating plant-microbe interactions and optimizing microbial network construction.