The Precambrian geologic record is characterized by a "long-tailed" data problem: vast volumes of information exist,yet it is often fragmented, sparse, and confined to specific spatiotemporal and disciplinary boundaries.This limits our ability to extract overarching knowledge about early Earth's tectonic regimes and habitability conditions. Big data science, coupled with machine learning (ML)， offers a transformative approach to overcome this challenge by enabling the integration and conjointanalysis ofdisparate,large-scaledatasets.This presentationdemonstratesthis paradigm through a comprehensive case study of the North China Craton (NCC). We integrate extensive rock and mineral geochemistry data and apply methods including local singularity analysisand wavelet analysis to zircon records.Thisreveals an-800-500 Myr periodicity since 3.5 Ga,suggesting NCC's co-evolution with the global supercontinent cycle since the Archean.To investigate the NCC's formation mechanism, we employed an ML-based reconstruction of crustal thickness evolution. The analysis identifies subduction-related arc formation as the primary driver,with secondary contributions from mantle activities. Further spatiotemporal analysis of magmatic intensity and cHf(t) values supports a model of prolonged accretion of arc massifs onto the Eastern Block. Our work exemplifies how big data and ML can unlock the latent potential in fragmented geologic records, providing a new, data-driven framework for interpreting the complex processes that shaped the early Earth.