In recent years, multi-view 3D reconstruction technology based on deep learning has become one of the research hotspots in the field of machine vision and is applied in many fields. However, the 3D reconstruction technology still has problems such as edge missing, serious network memory consumption and low reconstruction accuracy. In this paper, based on the existing problems of 3D reconstruction technology, EGF-MVSNet network is proposed based on deep learning. First, a feature extraction network incorporating the ECA attention layer is used to improve the network's attention to the channel feature information; then, an improved combination of GRU modules is used to obtain the GC regularization network for regularization and to reduce the computation of the network; finally, the SmoothL1 loss function and Adam optimizer are used to improve the convergence accuracy at the later stage of model training and to optimize the model's losses and parameters. Through testing and validation on the DTU public dataset, the EGF-MVSNet network proposed in this paper improves the completeness by 22.1% and the overall model score by 11.5% compared to the MVSNet network, which confirms that the EGF-MVSNet network can significantly improve the quality of the reconstruction results and reduce the network's consumption of memory.