Regarding constitutive behavior of concrete-like brittle materials subjected to high strain rates， we combine ABAQUS finite element simulations and back propagation （BP） artificial neural network method to analyze the critical waveform parameters in the split Hopkinson pressure bar （SHPB） experiments， and propose the machine-learning based methodology for predicting mechanical properties of concrete-like materials under high strain rates. This model significantly improves the computational efficiency to reveal the correlation mechanisms between deformation behavior and constitutive parameters of complex brittle materials under impact loading. We adopt the dynamic analysis module of ABAQUS finite element software to apply four different stress waves on the free surface of the incident bar to obtain the stress-strain curve of materials under different strain rates. By comparing with SHPB experimental data， the accuracy of numerical predictions from finite element simulations is validated. 20 sets of ABAQUS simulation results are exploited as training samples， in which the incident wave is used as the input layer while the transmitted and reflected waves are taken as the output layer. The results show that the machine-learning prediction model based on BP artificial neural network method owns satisfactory generality， and this proposed method could replace repetitive finite element modeling to considerably save the time for model creation， analysis and post-analysis process. It can accurately predict the constitutive behavior in the form of stress-strain curve for concrete-like materials under high strain rates， and can also predict the stress-strain responses under a wider range of strain rate beyond those provided from training samples.