Pathogenic bacteria in the water environment are mainly monitored in the public health, food safety, aquaculture and other industries due to their major threats to the health of humans and aquatic animals, and the biosafety of aquatic products. However, pathogenic database involved in water environment pathogen is mainly constructed according to independent disciplines, and scattered in the fields of clinical medicine and aquatic animal diseases, which can no longer meet the high-throughput identification and biosafety evaluation of pathogenic bacteria involved in the water environment in the regional scale or ecological perspective. In this study, a database of pathogenic bacteria involved in water environment (DPiWE) was constructed by collecting the taxonomic information of pathogenic bacteria from humans, aquatic animals, mammals, plants, and cross-host comorbidities. A multi-threaded schedulable communication model and a multi-task mode global sequence matching algorithm were developed to construct DPiWE. The database collected 9 070 pathogenic bacteria strains, which belong to 14 phyla, 27 classes, 54 orders, 116 families, 221 genera and 1 097 species. The corresponding 16S rRNA gene sequences, host information and infection types of these strains were also collected in DPiWE. This database was deployed at a website (http://dayuz.com/) with the functions including web user management, pathogenic information retrieval, sequence upload, storage and alignment, and visualization of annotation result. Two examples were used to test the functions of DPiWE. The first example showed that, DPiWE can accurately construct a phylogenetic network of an unidentified bacterium (strain DS10?D19) isolated from cultural seawaters, according to its 16S rRNA gene sequence, and identified it as Photobacterium leiognathid. The result of network also showed that the network structure of strain DS10?D19 was similar to P. leiognathid and P. angustum. The second example showed that the compositions of pathogens in the intestines of three mariculture animals were significantly different through annotating the high-throughput sequencing data using DPiWE, and the rearing water in diseased groups had potential risk of spreading the comorbid pathogenic bacteria of human and fish. The DPiWE and its supporting data analysis process can provide new ideas and data foundations for high-throughput detection of the biosafety of water environment, protecting health of fishery ecology, and controlling diseases of aquatic animals, in the future.