Species identification through DNA barcoding or metabarcoding has become a keyapproach for biodiversity evaluation and ecological studies. However, the rapid accu-mulation of barcoding data has created some difficulties: for instance, global enqui-ries to a large reference library can take a very long time. We here devise a two-stepsearching strategy to speed identification procedures of such queries. This firstly usesa Hidden Markov Model (HMM) algorithm to narrow the searching scope to genuslevel and then determines the corresponding species using minimum genetic distance.Moreover, using a fuzzy membership function, our approach also estimates the credi-bility of assignment results for each query. To perform this task, we developed a newsoftware pipeline, FuzzyID2, using Python and C++. Performance of the new methodwas assessed using eight empirical data sets ranging from 70 to 234,535 barcodes.Five data sets (four animal, one plant) deployed the conventional barcode approach,one used metabarcodes, and two were eDNA-based. The results showed mean accu-racies of generic and species identification of 98.60% (with a minimum of 95.00%and a maximum of 100.00%) and 94.17% (with a range of 84.40%–100.00%), respec-tively. Tests with simulated NGS sequences based on realistic eDNA and metabar-code data demonstrated that FuzzyID2 achieved a significantly higher identificationsuccess rate than the commonly used Blast method, and the TIPP method tends tofind many fewer species than either FuzztID2 or Blast. Furthermore, data sets withtens of thousands of barcodes need only a few seconds for each query assignmentusing FuzzyID2. Our approach provides an efficient and accurate species identifica-tion protocol for biodiversity-related projects with large DNA sequence data sets.