Abstract: Presently, the 1:50000 geological mapping completed in China is mainly distributed in the bedrock-outcropped area, rarely involving the coverage area which accounts for more than one-third of the national land area.To meet and adapt to the needs of national economic construction for a geological survey in the new era, the geological mapping must be extended to the covered area in the future.Making full use of big data, cloud computing, artificial intelligence and other technologies, the fusion and comprehensive analysis of massive multi-source heterogeneous geological data according to the requirements of geological mapping by computer data extraction is the key to realize the intelligent geological mapping in coverage areas.The experiment was carried out in the shallow covered forest-swamp region.The structured data such as aeromagnetic surveys, soil geochemistry, and non-structural data such as remote sensing image and surface geological surveys were fully utilized to carry out the comparative experiment on two algorithm models of cluster analysis and human-computer interaction deep learning.The results show that the clustering analysis based on single data cannot effectively divide the geological units, and the prediction model obtained by human-computer interactive deep learning and training with multi source data is basically consistent with the actual geological units.The machine learning function and special algorithm were used in this experiment and realized the geological mapping exploration by computer instead of geological personnel.It provides a demonstration case for primary geological map, work layout and achievement integration of the geological mapping in forest-swamp area, and offers a reference for the intelligent geological mapping in the coverage area.