Constructing a prediction model for archaeological sites can accurately identify the potential spatial extent of archaeological sites, which can help to prospectively protect undiscovered archaeological sites. A collection of 1 916 Neolithic archaeological sites (excluding burial sites) in Shandong Province was studied in this paper, with 1916 non-site locations used as negative controls (at a 1:1 ratio). Eight natural geographic environmental influencing factors (elevation, slope, slope orientation, profile curvature, planar curvature, micro-geomorphology, slope position, and distance to water boundaries) were selected to construct an index system for archaeological site prediction model. Using GIS spatial analysis method and the XGBoost algorithm, we constructed a predictive model for archaeological sites, which is used to predict the spatial extent of potential sites and analyze the importance of influencing factors. The archaeological site prediction model constructed with XGBoost algorithm achieves high accuracy, with an AUC value of 0.85. The model trained with optimal samples categorizes the results into low, medium, and high grades, mapped the spatial distribution of site probabilities, and revealed that sites primarily cluster in plains. Based on the XGBoost algorithm for ranking importance of influencing factors, slope position, microgeomorphology and elevation are the top three factors influencing the siting of Neolithic archaeologicalsites. The XGBoost algorithm has good stability and prediction ability. The constructed model provides a new research method for prediction of archaeological sites, and provides important technical support for field archaeological excavation and elucidates the relationship between prehistoric settlement patterns and geographical environments.