The Taihu Lake Basin in southeastern China represents a crucial cradle of prehistoric civilization, having sustained extensive human occupation since the Neolithic period. An archaeological site predictive model was developed for the Suzhou area by integrating multisource geospatial data—including remote sensing imagery, topographic parameters, and hydro-geomorphic factors—within a combined frequency ratio (FR) and maximum entropy (MaxEnt) modeling framework. To address limitations in factor weighting inherent in conventional FR approaches, an innovative conditional attention mechanism was incorporated to construct an enhanced weighted model (AM_FR), which quantified the sensitivity of various environmental predictors and optimized their contributions. Model performance was systematically evaluated and compared using training and testing datasets. The results show that elevation, surface roughness, and relief intensity are the primary environmental determinants influencing site location. The AM_FR model achieves higher area under curve (AUC) values on both training and testing datasets compared to standard FR and MaxEnt models, demonstrating superior fitting accuracy and predictive capability. Spatial overlay and reclustering analysis reveal that high-probability areas are predominantly distributed on gentle slopes near lakeshores with moderate elevations and reliable water access, consistent with the settlement principle of “adapting to habitat and leveraging natural advantages”. Guided by the established model, a new site is discovered. The research results validate the effectiveness of machine learning methods in archaeological predictive modeling. The proposed AM_FR approach offers a scientifically sound methodology and technical framework for improving the efficiency of regional archaeological survey and cultural heritage conservation practices.