Short　answer　scoring　is　a　significant　task　in　natural　language　processing.　On　datasets　comprising　numerous　explicit　or　implicit　symbols　and　quantization　entities,　the　existing　approaches　continue　to　perform　poorly.　Additionally,　the　majority　of　relevant　datasets　contain　few-shot　samples,　reducing　model　efficacy　in　low-resource　scenarios.　To　solve　the　above　issues,　we　propose　a　Multi-level　Semantic　Inference　Model　(M-Sim),　which　obtains　features　at　multiple　scales　to　fully　consider　the　explicit　or　implicit　entity　information　contained　in　the　data.　We　then　design　a　prompt-based　data　augmentation　to　construct　the　simulated　datasets,　which　effectively　enhance　model　performance　in　low-resource　scenarios.　Our　M-Sim　outperforms　the　best　competitor　models　by　an　average　of　1.48　percent　in　the　F1　score.　The　data　augmentation　significantly　increases　all　approaches＇　performance　by　an　average　of　0.036　in　correlation　coefficient　scores.