The multi-energy computed tomography (CT) technique can resolve the absorption rates of various energy X-ray photons in human tissues, representing a significant advancement in medical imaging. By addressing the challenge of swift degradation in reconstructed image quality, primarily due to non-ideal effects such as quantum noise, a dual-stream Transformer network structure is introduced. This structure utilises the shifted-window multi-head self-attention denoising approach for projection data. The shifted windows Transformer extracts the global features of the projection data, while the locally-enhanced window Transformer focuses on local features. This dual approach capitalizes on the non-local self-similarity of the projection data to maintain its inherent structure, subsequently merged by residual convolution. For model training oversight, a hybrid loss function incorporating non-local total variation is employed, which enhances the network model's sensitivity to the inner details of the projected data. Experimental results demonstrate that our method's processed projection data achieve a peak signal to noise ratio (PSNR) of 37.7301 dB, structure similarity index measurement (SSIM) of 0.9944, and feature similarity index measurement (FSIM) of 0.9961. Relative to leading denoising techniques, the proposed method excels in noise reduction while preserving more inner features, crucial for subsequent accurate diagnostics.