The statistical analysis of 4key performance indicators(compressive strength, splitting tensile strength, porosity and water permeability) of recycled aggregate pervious concrete(RAPC) revealed that these indicators basically obey the normal distribution pattern; based on this, the statistical characteristics and the intrinsic relation of its macroscopic properties were established. On this basis, and based on the artificial neural network method, the backpropagation(BP) network model was established by means of Python software. The model was used to conduct interactive prediction analysis on the key performance indicators of the concrete; the relative error of the average predicted values is within 10%. This indicates that the application of the method can achieve higher accuracy in performance prediction; it indicates that there is an inherent inverse relationship between the permeability and strength properties of RAPC and it is predictable.