The regenerator is the core component of the regenerative cryogenic refrigerator, for its structure sizes, operating parameters and phase characteristics at the cold and hot ends co-determine the power and efficiency of the refrigerator, and the design parameters of other coupled components. Efficiently predicting the regenerator performance can reduce the design period of cryogenic refrigerators. Addressing the long computational time constraints in the traditional numerical simulation methods, a novel approach based on a one-dimensional convolutional neural network (1D-CNN) was proposed. Initially, a program capable of multi-threading and automatically running the specialized regenerator calculation software REGEN 3.3 was developed. The performance of the regenerators with various parameter combinations at the cold end temperature of 60-120 K were calculated and 181,440 pieces of data were obtained. Subsequently, the architecture and hyperparameters of the model were determined. The trained model exhibits an average relative error of 3.83% for predicting regenerator power, 0.13% for predicting pressure ratio at the hot end, and 1.55% for predicting the coefficient of performance (COP). The model's generalization ability was confirmed by generating data points beyond the original dataset. Additionally, the model allows for the simultaneous calculation of multiple sets of irregular regenerator parameters, and reduces the calculation time from 2500 min for 1000 pieces using REGEN 3.3 software to just 130 ms, representing a decrease by nearly six orders of magnitude. This approach effectively resolves the long computation time associated with traditional numerical simulation methods, and will present a new solution for the rapid and precise design of regenerators.