<p>Previous research has demonstrated that applying taxonomic weights to shotgun metagenomic data can improve species identification in 16S rRNA gene-based microbiome analysis. However, such an approach does not allow for accurate analysis of samples collected from less studied habitats, such as rumen. In the present study, we developed a method to incorporate taxonomic weights based on relative abundance of species identified from shotgun sequencing and amplicon sequencing data derived from rumen. Using this weighting method, we evaluated latest versions of five prominent databases—SILVA, Greengenes2 (GG2), RDP, NCBI RefSeq, and GTDB—against the BLAST 16S rRNA database, assessing classification counts, fully classified ratios (proportion of ASVs classified to a known genus and species), and error rates. Our results indicated that providing taxonomic weights partially increased classification counts and fully classified ratios, although the extent of improvement varied across databases. A reduction in error rates was also observed compared to the unweighted taxonomy classifier (P &lt; 0.05). While GG2 and SILVA struggled with accurate classification at the species level owing to their inherent database characteristics, GTDB consistently improved all metrics using the manually weighted taxonomy classifier, achieving up to an 8% error rate reduction at the species level. NCBI RefSeq and RDP also exhibited remarkable improvement in the classification counts and fully classified ratios, along with error rate reductions by up to 47% at the species level. These findings demonstrate that amplicon sequencing datasets can enhance rumen microbiome analyses through effective weighting methods. While SILVA is commonly used in metataxonomic analyses of the rumen microbiome, we recommend NCBI RefSeq for species-level classification due to its superior accuracy and minimal ambiguous classification (e.g., “uncultured” or “sp.“) in future metataxonomic studies.</p>