The dehazing task was divided into two subtasks, content information extraction and edge feature restoration, and a two-branch feature joint dehazing network based on multidimensional collaborative attention was proposed, aiming at the problems that the single-feature extraction network had difficulty in collaboratively enhancing content and edge features in hazy image restoration. In the first branch of the network, densely connected convolutional blocks were constructed to extract multi-level content information from hazy images. In the second branch, cascaded multi-scale residual convolutional blocks were used to restore the texture details. Then the image reconstruction module was used to perform the multi-scale reconstruction on the two types of features, achieving the exchange and enhancement of different feature information, and improving the dehazing effect. Attention mechanism was introduced into the network and attention interactions were modeled in space and pixels to efficiently learn the main features of hazy images. The experimental results show that the proposed network outperforms most existing algorithms in objective metrics on multiple datasets, with high content restoration and complete texture details in dehazing visual effects.