Introduction Soybean root rot, a devastating soil-borne disease, severely limits global soybean yield and quality. Traditional control measures cause environmental pollution and have regional limitations. Root-associated microbiomes are vital for plant health, but most studies use relative abundance sequencing that distorts actual microbial quantities. This study used absolute quantitative high-throughput sequencing to clarify soil chemical properties and bacterial community assembly in healthy and diseased soybeans, laying a theoretical foundation for microbiome-based root rot management. Methods Samples were collected from bulk soil, and the endosphere, rhizoplane, rhizosphere of healthy and diseased soybeans in black soil fields of Heilongjiang. Soil chemical properties, including pH, soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), and available potassium (AK), were determined. Absolute quantitative sequencing of bacterial 16S rRNA V4–V5 region was performed, combined with qPCR for absolute abundance calibration. Bioinformatics analyses included α/β diversity, co-occurrence network, community assembly (βNTI RCbray), random forest and correlation analysis to identify key taxa and their relationships with environmental factors. Results Root rot significantly reduced rhizosphere SOC (by 29.13%), TN (8.57%), AN (24.18%), AP (18.86%), while increased AK (12.82%) and pH. However, the contents of certain bacterial taxa at the genus levels showed significant differences in both absolute and relative abundances. The bacterial co-occurrence network indicate that the interaction in the healthy soybean (H) group was more complex than that in the diseased soybean (S) group. Specifically, 1 module hub and 21 connectors were identified in the H group, whereas 55 connectors were detected in the S group. Community assembly in both the H and S groups was governed by deterministic processes, with homogeneous selection primarily observed in the S group. Random forest and correlation heatmap analyses revealed ASV115 ( Candidatus Koribacter ) in H group was positively correlated with SOC, pH and AN; ASV16 ( Streptomyces ), ASV42 ( Agrobacterium ) and ASV46 ( Mesorhizobium ) were keystones in S group. Discussion Root rot destroyed rhizosphere nutrient balance and reshaped bacterial community structure, reducing network complexity. Absolute quantification effectively compensated for the defects of relative abundance, accurately reflecting community changes. These findings clarify the microecological mechanism of soybean root rot, supporting the development of biocontrol strategies targeting rhizosphere microbiome for sustainable soybean production.
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