The automatic segmentation method for brain tumors based on a U-shaped network structure often suffers from information loss due to multiple convolution and sampling operations， resulting in suboptimal segmentation results. To address this issue， this study proposed a feature alignment unit that utilizes semantic information flow to guide the up-sampling feature recovery and design designed a lightweight Dual Attention Feature Alignment Network （DAFANet） based on this unit.Firstly， to validate its effectiveness and generalization， the feature alignment unit was introduced separately into three classic networks， namely 3D UNet， DMFNet， and HDCNet. Secondly， a lightweight dual-attention feature alignment network named DAFANet was proposed based on DMFNet. The feature alignment unit enhanced feature restoration in the up-sampling process， and a 3D Expectation-Maximization attention mechanism was applied to both the feature alignment path and cascade path to capture the full contextual dependency. The generalized Dice loss function was also used to improve segmentation accuracy in the case of data imbalance and accelerate model convergence.Finally， the proposed algorithm is validated on the BraTS2018 and BraTS2019 public datasets， achieving segmentation accuracies of 80.44%， 90.07%， 84.57% and 78.11%， 90.10%， 82.21% in the ET， WT， and TC regions， respectively.Compared to current popular segmentation networks， the proposed algorithm demonstrates better segmentation performance in enhancing tumor regions and is more adept at handling details and edge information.