<p>Seismic-induced sand liquefaction represents a high-impact geohazard, rendering the discrimination and prediction of sand liquefaction states essential for geohazard mitigation research. For the rational discrimination of sand liquefaction states, this study proposes an SSA-CNN-SVM model that integrates Sparrow Search Algorithm (SSA)-optimized Convolutional Neural Networks (CNN) with Support Vector Machines (SVM) for liquefaction discrimination. This model initiates from raw sand liquefaction data, accomplishes layer-by-layer learning to extract liquefaction features and discriminate the states of liquefaction, and employs SVM in lieu of Softmax functions for liquefaction state classification. This study integrated raw sand liquefaction data from the Tangshan earthquake and datasets from two other journal articles, constructing a comprehensive sample set comprising 300 instances. The evaluation metrics—standard penetration test (SPT) blow count, mean particle size, coefficient of uniformity, groundwater table depth, effective overburden pressure, seismic intensity, and cyclic shear stress ratio—were input into the SSA-CNN-SVM model for prediction. The predictions were compared with those from SSA-SVM, SVM, CNN, and Backpropagation Neural Network (BPNN) models, validated against actual sand liquefaction data. The results indicate that the SSA-CNN-SVM model demonstrates superior performance in sand liquefaction discrimination, achieving an accuracy of 88.33%, a precision of 86.19%, a recall of 89.44%, and an F1-Score of 87.89%—all exceeding the corresponding metrics of the other comparative models. This validates the high precision of the proposed liquefaction discrimination model and provides a novel approach for practical applications.</p>