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For the current situation that the amount of equipment test data is limited and the equipment test data is prone to missing, we propose a regression interpolation method based on ensemble learning algorithm. The Random Forests and XGBoost algorithms are used as regressor for interpolating the missing data by setting fast filling benchmarks and feature importance assessment strategies to improving data subset reconstruction and iterative partitioning strategies for the training and test sets, and automatically optimizing regressors hyperparameters via the Optuna framework. Based on this method, a type of missile launch trial were used for validating. The results show that the regression interpolation effect of the ensembel learning algorithm is significantly better than the traditional statistical interpolation method as well as KNN and BP neural networks. And the R square different missing proportions are maintained above 0.95, which can effectively solve the problem of missing data of small sample tests of equipment. In addition, We validate the generalizability of this method by using the KEEL public test dataset.