In the task of 3D reconstruction, it is difficult to deal with the traditional multi view stereo algorithm because of the large photometric consistency measurement error in the weak texture region. To solve this problem, a recursive convolution network of multi-scale feature aggregation was proposed, named MARDC-MVSNet (multi-scale aggregation recursive multi view stereo net with dynamic consistency), which was utilized for dense point cloud reconstruction in weak texture areas. In order to boost the resolution of the input image, this method used a lightweight multi-scale aggregation module to adaptively extract image features, thereby addressing the problem of weak texture or even no texture region. In terms of cost volume regularization, a hierarchical processing network with recursive structure was used to replace the traditional 3D CNN (convolutional neural networks), greatly reducing the occupation of video memory and realizing high-resolution reconstruction at the same time. A depth residual network module was added at the end of the network to optimize the initial depth map generated by the regularized network under the guidance of the original image, so as to produce more accurate expressions of the depth map. The experimental results show that excellent results were achieved on the DTU data set. The proposed network can not only achieve high accuracy in depth map estimation, but also save hardware resources, and it can be extended to aerial images for practical engineering.