Objective:To investigate the conversion of low-energy CBCT images into high-energy CBCT images in clinical radiotherapy based on the deep learning method of U-Net network, in order to provide dual-energy CBCT images and reduce radiation dose.Methods:The CBCT image data of CIRS electron density phantom and CIRS head phantom at 80 and 140 kV were collected by the on-board CBCT in radiotherapy equipment. The dataset was divided into training set and test set according to 10∶1. The U-Net network was used to predict CBCT images at high energy (140 kV) from low-energy (80 kV) CBCT images. Four parameters, including mean absolute error (MAE), structural similarity index (SSIM), signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR) were used to quantitatively evaluate predicted high-energy CBCT images.Results:The overall structural difference between the predicted high-energy image and the real high-energy image was smaller (SSIM: 0.993 ±0.003). The noise of predicted high-energy image was lower (SNR: 15.33±4.06), but there was a loss of inter-tissue resolution. Predicted high-energy images had slightly lower average CT values than real high-energy images, with less difference in low-density tissues (<10 HU, P > 0.05) and greater differences in high-density tissues (<21 HU, t = -7.92, P < 0.05). Conclusions:High-energy CBCT images with high structural similarity can be obtained from energy CBCT images by using deep learning method. The predicted high energy CBCT images have the potential to be applied to clinical dual-energy CBCT imaging technology in radiotherapy.