To address the imbalance between efficiency and accuracy in Neural implicit Surface （NeuS） reconstruction caused by dense sampling and volumetric rendering， a fast and efficient reconstruction method was proposed. Firstly， dynamic occupancy grids were combined with density thresholds to optimize and select effective sampling points， thereby reducing redundant computation and memory usage. Then， multi-resolution hash encoding was integrated with Truncated Signed Distance Field （TSDF） to enhance gradient stability and noise resistance ability through truncated distance constraint and hash feature interpolation. Finally， photometric consistency constraint was introduced to optimize across-view geometric consistency using Normalized Cross-Correlation （NCC）， thereby improving the quality of surface reconstruction. Experimental results show that on the synthetic dataset NeRF-Synthetic， the proposed method reduces the training time by 98.8% compared to Neural Radiance Field （NeRF）. Although the training time of the proposed method is longer than that of Instant-NGP （Instant Neural Graphics Primitives） accelerated by CUDA （Compute Unified Device Architecture）， it achieves significantly superior surface reconstruction quality. On the real dataset DTU， the method achieves a surface reconstruction Chamfer Distance （CD） and a new-view synthesis Peak Signal-to-Noise Ratio （PSNR） significantly better than most existing methods， with CD reduced by 1.21 mm and PSNR higher by 2.93 dB compared to Instant-NGP. Though the metrics of the proposed method are slightly worse than Neuralangelo （with CD higher by 0.02 mm and PSNR lower by 2.05 dB）， the training time of the proposed method is reduced by 97.5% compared to Neuralangelo. It can be seen that this method can improve training efficiency and ensure reconstruction accuracy effectively with good practicality， and is suitable for efficient 3D reconstruction in complex structural scenes.