Understanding the mechanical behavior of hybrid fiber-reinforced concrete (HFRC), a composite material, is crucial for the design of HFRC and HFRC structures. In this study, a series of compression experiments were performed on hybrid steel- polyvinyl alcohol (PVA) fiber-reinforced concrete containing fly ash and slag powder, with a focus on the fiber content/ratio effect on its compressive behavior; a new approach was built to model the compression behavior of HFRC by using an artificial neural network (ANN) method. The proposed ANN model incorporated two new developments: the prediction of the compressive stress-strain curve and consideration of 23 features of components of HFRC. To build a database for the ANN model, relevant published data were also collected. Three indices were used to train and evaluate the ANN model. To highlight the performance of the ANN model, it was compared with a traditional equation-based model. The results revealed that the relative errors of the predicted compressive strength and strain corresponding to compressive strength of the ANN model were close to 0, while the corresponding values from the equation-based model were higher. Therefore, the ANN model is better able to consider the effect of different components on the compressive behavior of HFRC in terms of compressive strength, the strain corresponding to compressive strength, and the compressive stress-strain curve. Such an ANN model could also be a good tool to predict the me-chanical behavior of other composite materials.