In the meta-barcoding based eDNA monitoring technology, the analysis and annotation of eDNA sequence data serve as the foundation for obtaining accurate and reliable monitoring results. The selection of reference databases, the determination of analysis and annotation indicator thresholds, and the preparation of target data are the most critical technical steps in eDNA sequence data analysis and annotation. To clarify the impacts of these three technical aspects and provide scientific support for the standardization of eDNA monitoring technology, the current study used two sets of COI gene sequence data from eDNA monitoring in the middle reach of the Yangtze River as the analysis objects and designed three sets of experiments to test 1) the impacts of different reference databases and species annotation algorithms on the annotation results, 2) the impacts of different OTU clustering sequence similarity and species annotation classification confidence (sequence consistency and sequence coverage) on the annotation results, and 3) the impacts of different target sequence data richness of each species on the annotation results. The results showed that: 1) under the Blast algorithm, the annotated species matched with three versions of nt library from NCBI were generally consistent (72%-78%); those matched with two local sequence reference libraries were also generally consistent (91%-96%); and the annotated species from the five results matched with these five sequence reference libraries were consistent in 52%-68%. The RDP Classifier algorithm annotated species matched with nt libraries covered over 95% of Blast algorithm annotated species, and increased by 151%-443% species, but most additional species were misannotated. The RDP Classifier algorithm annotated species matched with local sequence reference libraries covered 66%-85% of Blast algorithm annotated species, and there were several results only annotated to family or genus level. 2) When the OTU clustering sequence similarity threshold was set to 0.999, it obtained 154%-209% more OTUs than when set to 0.99, and 240%-490% more annotated OTUs of fish were obtained. The classification confidence threshold (Blast algorithm) had little effect on species composition when changed from 0.8 to 0.99, with over 94% consistency, but there was a significant difference when it was set to 0.7. 3) When the OTU clustering sequence similarity threshold was 0.999 and the classification confidence threshold was 0.9, the number of fish species and OTUs obtained from multiple-sequences data annotation was the largest. It also had the highest species annotation accuracy (81.49%), which increased by 7% fish species, 215% OTUs and 5% accuracy respectively compared to single-sequence data annotation. In eDNA sequenc data analysis and annotation, accuracy can be improved by establishing and improving local reference databases, optimizing OTU clustering sequence similarity and species annotation classification confidence thresholds (sequence consistency and sequence coverage), increasing target sequence data richness. However, due to the limitation of species annotation algorithms, problems such as species annotation errors and omissions may persist in eDNA sequence data analysis and annotation in the future. Then, the species annotation accuracy of eDNA monitoring (based on the COI gene) would always be lower than 85%.