为解决煤矿深部井巷工程巷道掘进顶板空顶期沉降量的预测问题，引入人工智能的支持向量机(SVM)工具，结合麻雀搜索优化算法(SSA)，提出基于SSA−SVM的巷道顶板空顶沉降量预测模型。以内蒙古长城五矿深部地下巷道掘进过程的顶板空顶期位移量数据作为样本集合，选择单轴抗压强度(UCS)、岩石完整性(RQD)、地应力、巷道宽跨比、空顶时间、人为采动 6项影响因素，通过适用性、相关性和归类一致性评价对数据的综合影响权重进行归纳整理。将十折交叉验证的准确率作为适应度函数，对不同种群数量的SSA−SVM预测模型展开训练和测试，通过误差相关系数(RMSE、MAPE、R²)、ROC曲线、AUC ± Std、运行时间以及标准偏差率η等5方面来选择种群数量最优参数模型，并将该模型应用于1902S回风巷进行巷道掘进顶板空顶期的沉降量预测，同巷道实际矿压监测数据进行比较。研究结果表明：当种群数量为90时，SSA−SVM模型预测性能较好，训练样本的RMSE为0.0165，MAPE为22.54%，R²为0.8295；测试样本的RMSE为0.0156，MAPE为22.37%，R²为0.8490；真实度AUC达到最大0.8467，离散度Std最小为0.0115；运行时间最短为8.7239 s；标准偏差率维持在0.12%。在1902S回风巷现场应用中，预测值与实际值没有出现较大偏差，维持在线性拟合y=0.90x和y=1.10x范围内，误差相关系数与AUC ± Std均符合试验精度要求，该模型的预测效果能够对后续的支护设计及补强支护作业提供重要的指导。

In order to solve the prediction problem of the roof settlement in the unsupported period of roadway excavation in the deep roadway engineering of coal mines, the support vector machine (SVM) tool of artificial intelligence was introduced. Combined with the sparrow search optimization algorithm (SSA), a prediction model of the roof settlement during its unsupported period based on SSA-SVM was proposed. Taking the displacement data during the roof’s unsupported period in the deep underground roadway excavation process of the Great Wall No.5 Mine in Inner Mongolia as a sample, six influencing factors including uniaxial compressive strength (UCS), rock integrity (RQD), ground stress, roadway width-span ratio, non-support roof time and artificial mining were selected. The comprehensive influence weights of the data were summarized through applicability, correlation and classification consistency evaluation. The accuracy of ten-fold cross-validation was used as the fitness function to train and test the SSA-SVM prediction model with different population numbers. The optimal parameter model of population number was selected by error correlation coefficient (RMSE, MAPE, R²), ROC curve, AUC ± Std, running time and standard deviation rate η. The model was applied to the 1902S return airway to predict the settlement of the roadway during the roof’s unsupported period, and compared with the actual mine pressure monitoring data of the roadway. The results show that the prediction performance of SSA-SVM model is better when the population number is 90. The RMSE of training samples is 0.0165, MAPE is 22.54%, and R² is 0.8295. The RMSE of the test sample is 0.0156, MAPE is 22.37%, and R² is 0.8490. The realism AUC reaches the maximum of 0.8467, and the standard deviation Std is the minimum of 0.0115. The shortest running time is 8.7239 s and the standard deviation rate is maintained at 0.12%. In the field application of the 1902S return airway, there is no large deviation between the predicted value and the actual value, which is maintained in the range of linear fitting y = 0.90x and y = 1.10x. Both the error correlation coefficient and AUC ± Std satisfy the requirements of test accuracy. The prediction effect of this model can provide an important guidance for subsequent support design and reinforcement of support measures.