为了分析和预测砂灰比、料浆浓度、矿渣粉占比影响下的充填体抗压强度，采用响应面法对17组充填体试件进行单轴压缩试验，分析单因素及多因素交互作用对充填体3、7、28 d抗压强度的影响，优化材料配比，并建立基于残差连接长短期记忆网络（long short-term memory, LSTM）充填体强度预测模型。结果表明：砂灰比、料浆浓度、矿渣粉占比对充填体强度有显著影响，砂灰比与矿渣粉占比交互作用对充填体7、28 d强度影响显著；料浆浓度与矿渣粉占比交互作用对充填体28 d强度影响显著；为满足充填强度要求，经模型优化后充填体28 d强度为3.04 MPa，重复试验验证相对误差为3.12%；基于残差连接LSTM构建了以砂灰比、料浆浓度、矿渣粉占比为输入变量，充填体强度作为输出变量的预测模型。模型均方根误差（root mean square error, RMSE）为0.45，决定系数（R²）为0.999，平均绝对误差（mean absolute error, MAE）为0.054。预测精度优于传统LSTM网络模型，能精确地预测充填体抗压强度。

In order to analyze and predict the compressive strength of backfill under the influence of sand-cement ratio, slurry concentration and slag powder proportion, uniaxial compression test was carried out on 17 groups of backfill specimens by the response surface method. The influence of single factor and multi-factor interaction on the compressive strength of backfill at 3, 7 and 28 days was analyzed, and the material ratio was optimized. The filling strength prediction model based on residual connection long short-term memory (LSTM) network was established. The results show that the sand-cement ratio, slurry concentration and slag powder ratio have significant effects on the strength of backfill, and the interaction between sand-cement ratio and slag powder ratio has significant effects on the strength of backfill at day 7 and day 28. The interaction between slurry concentration and slag powder ratio has significant influence on 28-day strength of backfill. In order to meet the requirements of filling strength, the strength of backfill body after 28 days of model optimization is 3.04 MPa, and the relative error of repeated tests is 3.12%. Based on residual connection LSTM, a prediction model with sand-cement ratio, slurry concentration and slag powder ratio as input variables and backfill strength as output variables was constructed. The model root mean square error (RMSE) is 0.45, coefficient of determination (R²) is 0.999, and mean absolute error (MAE) is 0.054. The prediction accuracy is better than the traditional LSTM network model, and it can accurately predict the compressive strength of backfill.