The study of microbiota has been revolutionized by the development of DNA metabarcoding. This sequence-based approach enables the direct detection of microorganisms without the need for culture and isolation, which significantly reduces analysis time and offers more comprehensive taxonomic profiles across broad phylogenetic lineages. While there has been an accumulating number of researches on bacteria, molecular phylogenetic analysis of fungi still remains challenging due to the lack of standardized tools and the incompleteness of reference databases limiting the accurate and precise identification of fungal taxa. Here, we present a DNA metabarcoding workflow for characterizing fungal microbiota with high taxonomic resolution. This method involves amplifying longer stretches of ribosomal RNA operons and sequencing them using nanopore long-read sequencing technology. The resulting reads were error-polished to generate consensus sequences with 99.5–100% accuracy, which were then aligned against reference genome assemblies. The efficacy of this method was explored using a polymicrobial mock community and patient-derived specimens, demonstrating the marked potential of long-read sequencing combined with consensus calling for accurate taxonomic classification. Our approach offers a powerful tool for the rapid identification of pathogenic fungi and has the promise to significantly improve our understanding of the role of fungi in health and disease.