Abstract: The prediction and evaluation of deep-sea polymetallic nodules have entered into the data science paradigm, and the deep mining and fusion of big data for ore prospecting and ore indication are seriously needed. Through analyzing the research progress of  deep-sea polymetallic nodules evaluation, and discussing the application of big data approaches in mineral resources evaluation, the big data mining and fusion techniques towards resources evaluation of  polymetallic nodules are explored, in which, the major research contents and methods like the knowledge pedigree analysis for polymetallic nodules resources, the metallogenic characteristics mining methods based on data science, the fusion and integration methods based on spatial decision-making model with big data, the comparison and verification of quantitative prediction and evaluation on polymetallic nodules resource, are proposed. It creatively analyzes  conventional/unconventional resources evaluating data and its correlation with ore deposits, and establishes the spatial decision-making model with geological constraint and big data. On this basis,  feature extraction and fusion of the information from multi-source heterogeneous data are achieved to supply the technology solution based on big data for deep-sea mineral resources evaluation. The research on big data mining and fusion techniques could improve the accuracy and efficiency of polymetallic nodules resources evaluating, and has important theoretical value and practical significance to the efficient use of deep-sea resource-environmental data, the exploration and evaluation of new polymetallic nodules mining area, and the prediction and evaluation of other deep-sea minerals.

Key words: polymetallic nodules, resources evaluation, deep-sea minerals, big data, data mining, data fusion