A　data-driven　indirect　approach　for　predicting　the　response　of　existing　structures　induced　by　excavation　is　hereby　proposed　based　on　making　full　use　of　monitoring　data　during　excavation,　which　can　predict　the　deformation　history　of　the　research　object　during　excavation.　In　this　article,　a　machine-learning-based　model　framework　for　implementing　the　proposed　approach　is　constructed　and　the　treatment　of　key　issues　in　the　design　and　implementation　of　the　proposed　method　is　described　in　detail　including　the　theoretical　framework,　the　implementation　mode　of　the　method,　the　dimensionality　reduction　of　the　model　parameters,　and　the　normalization　of　data　for　model.　On　this　basis,　three　models　are　provided　to　predict　the　settlement　of　buildings　induced　by　adjacent　excavation,　namely　the　SVM　model,　BP　model,　and　BP-SVM　model.　Relying　on　an　excavation　project　for　a　subway　in　Xuzhou,　Jiangsu　Province,　China,　the　proposed　method　is　verified,　and　some　conclusions　are　obtained.