Abstract: Based on the scattering center parameterized model and back propagation neural network, a far-field electric field prediction network for the target in full angles and wide frequency band is constructed in this paper. The fast prediction of the real and imaginary parts of the far-field electric field is realized by using the target position, amplitude, frequency and other data information through the network. Firstly, the position and intensity of the strong scattering points are extracted. Then, the two-dimensional angular domain and frequency domain are divided into regions. A parallel intelligent network architecture is constructed to establish the relationship between the scattering center parametric model and the high-precision far-field electric field. This method can reduce the frequency and angle dependence of the traditional scattering center model through the training of the novel parallel network. It realizes the rapid acquisition of the far-field electric field of the target. Because the influence of the scattering parameters in the existing model on the target electric field is fully used for reference in the process of network design, the neural network has clear physical significance and outstanding generalization ability. Compared with the traditional electric field reconstruction method based on geometrical theory of diffraction (GTD) model, this method has higher accuracy. The experimental results show that the proposed parallel network reduces the prediction error of electric field by more than 18%. At the same time, the error of the proposed parallel prediction network for the prediction of the target backward far-field electric field can be less than 5%.