The traditional “trial-and-error” method is used to reveal the relationship between microstructure and properties of superalloys, which is difficult to meet the rapid development of material design, research and development and application due to its high cost and long cycle. In this paper, a model for the relationship between microstructure and properties of superalloy K4169 was established by machine learning. Firstly, 70 groups of microstructures were obtained by changing the alloy composition and heat treatment system. Secondly, the mechanical properties of the alloys were measured by tensile experiments at room temperature and high temperature, and the effects of composition and heat treatment on the mechanical properties were analyzed. Finally, support vector machine regression (SVR), random forest regression (RFR), K-nearest neighbor node regression (KNR) and multi-layer perceptron (MLP) were used to establish prediction models to predict the effects of γ phase, γ′ phase, γ″ phase, δ phase, Laves phase and carbide content on tensile properties at room and high temperature. The accuracy of the model was verified by cross validation. The results show that the mean squared error of the MLP model for the prediction result of the alloy room temperature tensile strength is 0.17, the mean absolute error is 0.32, the correlation coefficient is 0.95, and the decision coefficient is 0.85.And the mean squared error for the alloy high-temperature tensile strength is 0.14, the mean absolute error is 0.29, the correlation coefficient is 0.97, and the decision coefficient is 0.91.Compared with the other three models, the prediction results of MLP are more accurate.