This study proposes a novel acceleration method for the Neural Radiance Field (NeRF) in dynamic 3D human reconstruction to address the challenges of low training efficiency and high computational complexity in volume rendering. To improve the ability of the NeRF to represent detailed local features, multiresolution hash encoding is used as positional encoding, which increases the NeRF's convergence speed. In addition, a shallow network is designed to estimate the volume density of the NeRF. An opacity loss function is proposed to optimize the network using the human alpha map output obtained by PP-Matting. The proposed density estimation network is used to compute the transmittance distribution along the camera rays during volume rendering. The importance sampling strategy for volume rendering is then implemented by inversely sampling the transmittance distribution, which reduces the number of unnecessary sampling points and improves the volume rendering's computational efficiency. Furthermore, precise human foreground masks are generated by binarizing human alpha maps, which enhances the quality of the reconstructed datasets. Extensive experiments demonstrate that the combination of multiresolution hash encoding and importance sampling strategy improves the reconstruction speed on the ZJU-MoCap and SHTU-MoCap datasets by 17.7%, 9.5%, and 37.5%, respectively, compared to the Neural Body, HumanNeRF, and MonoHuman, while also achieving higher reconstruction accuracy. The use of binarized PP-Matting increases the accuracy of human masks to over 96%.