High-throughput sequencing has driven an exponential expansion of global gene data (doubling approximately every seven months)， creating new opportunities for discovering high-performance biocatalysts. However， the high structural complexity of genomes substantially increases the difficulty of functional annotation， leaving a large fraction of sequences in massive datasets with insufficient high-quality， precise annotations. Conventional annotation strategies based on sequence alignment and homology inference provide only limited labels for highly similar sequences and face major bottlenecks when dealing with multifunctional enzymes (e.g.， catalytic promiscuity) and complex eukaryotic genome architectures such as nested genes and alternative splicing. This study developed an automated， cross-species genome annotation pipeline integrating gene structure prediction， domain identification， and metabolic pathway reconstruction， together with a function-oriented sequence mining framework based on multilocus phylogenetic analyses. Using these approaches， this study built a high-resolution phylogenetic gene-cluster database covering 430 000 bacterial and 20 000 fungal genomes. By analyzing multilocus tree topologies， this study precisely identified key enzymes in the r-BOX cycle for medium-chain fatty acid biosynthesis， including 17 135 fatty-acid β-oxidation multifunctional enzyme subunit(FadB)， 17 115 β-ketothiolase (BktB)， 18 985 enoyl-CoA reductase(Ter)， and 18 575 3-oxoacid CoA-transferase(YdiI)， none of which were found to be recorded in the NCBI database. To validate the utility of the database， this study targeted FadB and screened 10 uncharacterized homologs， of which three exhibited catalytic activity. Recombinant expression increased shake-flask production from 0.65 g/L to 1.1 g/L. In addition， this study deployed an openly accessible interactive platform (https://thezsd.github.io/) that enables user-driven enzyme mining and genome data submission to support accurate annotation. Collectively， this database-and-platform framework provided a data-driven solution for enzyme discovery and functional genomics in synthetic biology and metabolic engineering.