Scatter artifacts is one of the limitations of cone-beam computed tomography(CBCT) image quality, this paper proposed a novel scatter correction method for CBCT via combining the deep learning and forward projection algorithm. This method can be mainly divided follow steps: Firstly, raw projections were used to reconstruct raw CBCT via FDK algorithm, and then the raw CBCT was processed by CycleGAN network to generate synthetic CT, after that the synthetic CT was used to forward projection based on the Beer's laws to generate scatter free primary projections. The raw scatters can be estimated via subtracting the primary projections from the raw projections, and then a median and low-pass Gaussian filter was used to smooth the raw scatters. Finally, The scatter corrected projections can be acquired by subtracting the filtered scatters form raw projections. The study results of pelvis and chest validated the effective of proposed correction method in reducing scatter artifacts of CBCT. Compared with uncorrected CBCT and CBCT corrected by scatter kernel superposition(SKS) method, the proposed method can more effectively improve the quality of reconstructed CBCT, reducing the CT number errors and increasing contrast-to-noise(CNR) and so on. All results show that this method has strong scatter artifacts restriction ability, so it has significant promising for clinical application.