The number of metagenomes is increasing rapidly. However, current methods for metagenomic analysis are limited by their capability for in-depth data mining among a large number of microbiome each of which carries a complex community structure. Moreover, the complexity of configuring and operating computational pipeline also hinders efficient data processing for the end users. In this work we introduce Parallel-META 3, a comprehensive and fully automatic computational toolkit for rapid data mining among metagenomic datasets, with advanced features including 16S rRNA extraction for shotgun sequences, 16S rRNA copy number calibration, 16S rRNA based functional prediction, diversity statistics, bio-marker selection, interaction network construction, vector-graph-based visualization and parallel computing. Application of Parallel-META 3 on 5,337 samples with 1,117,555,208 sequences from diverse studies and platforms showed it could produce similar results as QIIME and PICRUSt with much faster speed and lower memory usage, which demonstrates its ability to unravel the taxonomical and functional dynamics patterns across large datasets and elucidate ecological links between microbiome and the environment. Parallel-META 3 is implemented in C/C++ and R, and integrated into an executive package for rapid installation and easy access under Linux and Mac OS X. Both binary and source code packages are available at http://bioinfo.single-cell.cn/parallel-meta.html.