This paper is the result of mineral exploration engineering.

Objective The current hotspots on big data are almost focused on how to discover or reveal the hidden value or secrets of big data, and there is an extreme lack of systematic, in−depth, and practical research or solutions on how to improve the quality of big data construction. However, the quality of big data construction is crucial for successfully discovering or revealing the hidden value or secrets of big data, and for making or implementing scientific and accurate decisions.

Methods Firstly, by analyzing and summarizing the existing relevant research results, including the quality control experiences of the big data construction of China's National Mineral Resource Potential Evaluation (2006−2013), the entity data models for ore−searching prognosis in mineralization concentrating areas, and the basic framework of quality control models in the field of earth science. Then, with the help of big data thinking and digital and intelligent technologies, a digital and intelligent system of the whole process and total factor quality control for ore−searching prognosis and big data entity construction in mineralization concentrating areas is established. Finally, a new paradigm of the whole process and total factor quality control is proposed, which can be extended and supported to the quality control of big data asset construction in the field of earth science and beyond.

Results This study has proposed the quality control theory and method on data division and graininessl, established a digital and intelligent system of the whole process and total factor quality control for ore−searching prognosis and big data entity construction in mineralization concentrating areas, developed quality control software, formulated standards and specifications for data quality check and evaluation, and efficiently supported and fulfilled the quality control works such as self check, mutual check, special check, supervision and check, field acceptance, initial review, final review, re−examination, and confirmation of acceptance for the construction of big data entities for ore−searching prognosis in mineralization concentrating areas.

Conclusions The digital and intelligent system of the whole process and total factor quality control for ore−searching prognosis and big data entity construction in mineralization concentrating areas has originality, practicality, efficiency and universality, which can directly be expand to and support the quality control of big data asset construction in the field of earth science and beyond. The proposed new paradigm of the whole process and total factor quality control also is scientific, effective, and universally applicable. The proposed concept of ontology constraint for data model has universality and practical significance, that is, the definition of data model should meet the requirements of conceptualization, sharing, explicitness, and formalization, applicable to two−dimensional or high−dimensional map−type data entities, as well as simple(e.g. data sheet) or complex(e.g. relation database)（relation database） table−type data entities.