Meng, Y.; Chen, Y.; Zhu, F., and Tian, E., 2020. The integration of marine biodiversity information resources based on big data technology. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 806–811. Coconut Creek (Florida), ISSN 0749-0208.

With the rapid development of the Internet of Things (IOT) technology, the data reserve of marine biological species diversity is gradually attracting people's attention. Therefore, based on big data technology and Hadoop architecture, this paper designs a marine life species information retrieval system using distributed BP neural network algorithm. The design idea is to optimize the BP neural network algorithm on the basis of the Map Reduce framework and the BP neural network in parallel, and to design the architecture of the marine organism species information system. Finally, after the building of the experimental environment based on the Hadoop, three sets of test data are selected to compare the optimized algorithm with the traditional BP neural network algorithm. The results show that the recall rate and accuracy of water quality classification between traditional and optimized algorithm are almost the same, indicating that the water quality classification ability of distributed BP neural network is pretty strong. The algorithm can be well applied in the research work of marine species diversity. Meanwhile, it can also help the management and data mining analysis of marine species.