The neural network comprises many neurons with extensive interconnections operatingparallel and performing specific functions. This paper establishes a BP neural networkprediction model for the compressive strength of CFRP-confined concrete based on a largenumber of experimental data to study the predictive ability of the BP neural network on thecompressive strength of CFRP-confined concrete and the output performance of the neuralnetwork model. The model is based on a BP neural network that has been trained using manyexperimental data. An investigation is being conducted on the effect of different datacombinations on the accuracy of the predictions made by the neural network model. Thehigh-precision BP network model is created into generic and simplified formulae for applicationconvenience. These formulas are developed based on the theory of neural networks. Theneural network models' findings and the empirical formulae for making predictions arecompared and discussed. The BP neural network accurately predicts the compressive strengthof CFRP-confined concrete, with over 90% of its data points having less than 15% error. Incomparison, the regression model shows less accuracy, with less than 70% of its data pointshaving an error within 15%. Compared to traditional regression models, the simple linearequation derived using Purelin instead of Sigmoid as the transfer function only adds a constantterm. The average value of prediction/test results is 1.011. The analysis results show that BPneural network can extract the input and output parameters' data information well and obtaina high-accuracy prediction model. The coefficient of variation is 0.112, which indicates thatthe prediction accuracy and stability are greater than average.