Settlement strategies and their driving mechanisms of Neolithic settlements using machine learning approaches: a case study in Zhejiang Province

Deciphering Neolithic settlement environmental selection strategies is vital for understanding prehistoric human-environment relationships. This study employs a multi-classification XGBoost model and SHAP analysis to accurately classify 432 Neolithic archaeological sites in Zhejiang Province (AUC = 0.93), effectively distinguishing environmental selection patterns across different cultural phases. The model’s feature importance ranking indicates that elevation, surface relief, slope, and water buffer zones are main factors influencing settlement site selection, though their impact intensity and mechanisms vary significantly across different cultural phases. Early Neolithic settlements (11.0–7.0 ka BP) favored high-altitude, vegetated river valleys supporting hunting-gathering economies, while mid-Neolithic communities (7.0–4.3 ka BP) shifted to lowland alluvial plains promoting rice agriculture. Late Neolithic settlements (4.3 ka BP-) expanded to higher elevations to mitigate flooding risks, coinciding with revived hunting-gathering practices. This study highlights the interplay between environmental and socio-economic factors in shaping settlement patterns and demonstrates the value of machine learning for archaeological research.