Hyperspectral imaging and Neural Radiance Field (NeRF) can be combined in powerful ways. With limited hyperspectral images, NeRF can generate images of objects with spectral information from arbitrary viewpoints, which can effectively mitigate defects such as long acquisition time and difficulty in obtaining hyperspectral images. This paper addresses challenges in the application of NeRF methods in the hyperspectral domain, including local errors in convergence caused by noise. Leveraging the characteristics of hyperspectral data, we propose a neural radiance field method employing a multi-channel volume density distribution function. This approach alleviates issues during the generation of neural radiance fields from hyperspectral data, enhancing the robustness of hyperspectral neural radiance field methods across various scenarios, which can help downstream tasks such as discriminating objects more effectively than RGB methods. Experiments demonstrate that the proposed method generates superior hyperspectral images under diverse conditions, with a maximum PSNR 37.66 and a maximum SSIM 0.982.