This paper proposes a part aligned multiscale fusion network, based on a human pose estimation algorithm and similarity matrix to address the misalignment problem of local features caused by complex person re-identification scenes and difficulty in extracting invariant person features in cluttered backgrounds. The proposed network introduces pose-estimation algorithms to construct aligned local features and integrates low-level local features and high-level global features through a multibranch structure. In addition, the feature similarity matrix divides the global features into the similarity-guided background and foreground branches and uses the regional-level triplet loss to extract person features robust to complex backgrounds. Extensive experiments are conducted for four datasets (Market-1501, DukeMTMC-ReID, CUHK03, and MSMT17). The proposed method achieves state-of-the-art performance. In particular, it improves the accuracy of first hit accuracy by 1.4 percentage points and mean average precision by 3.4 percentage points on the most challenging MSMT17 dataset.