Neural radiation field (NeRF) exhibits excellent performances in implicit 3D reconstruction compared with traditional 3D reconstruction methods. However, the simple multilayer perceptron (MLP) model lacks local information in the sampling process, resulting in a fuzzy 3D reconstruction scene. To solve this issue, a multifeature joint learning (MFJL) method based on MLP is proposed in this study. First, an MFJL module was constructed between the embedding layer and the sampling layer of NeRF to effectively decode the multiview encoded input and supplement the missing local information of MLP model. Then, a gated channel transformation MLP (GCT-MLP) module was built between the sampling layer and the inference layer of NeRF to learn the interaction relations between higher-order features and control the information flow fed back to the MLP layer for the selection of ambiguous features. The experimental results reveal that the NeRF based on the improved MLP can avoid blurred views and aliasing in 3D reconstruction. The average peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and learned perceptual image patch similarity (LPIPS) values on the Real Forward-Facing dataset are 28.08 dB, 0.887, and 0.061; on the Realistic Synthetic 360° dataset are 32.75 dB, 0.960, and 0.026; and on the DTU dataset are 25.96 dB, 0.807, and 0.208, respectively. Overall, the proposed method has a better view reconstruction performance and can obtain clearer images and detailed texture features in subjective visual effects compared with NeRF.