Unsupervised anomaly detection has been widely applied to industrial scenarios. Recently, transformer-based methods have also been developed and have produced good performance. Although the global dependencies in anomaly images are considered, the typical patch partition strategy in the vanilla self-attention mechanism ignores the content consistencies in anomaly defects or normal regions. To sufficiently exploit the content consistency in images, we propose the multi-granularity content-aware network with semantic integration (MGCA-Net), in which superpixel segmentation is introduced into feature space to divide images according to their spatial structures. Specifically, we adopt a pre-trained ResNet as the encoder to extract features. Then, we design content-aware attention blocks (CAABs) to capture the global information in features at different granularities. In this block, we impose superpixel segmentation on the features from the encoder and employ the superpixels as tokens for the learning of global relationships. Because the superpixels are divided according to their content consistencies, the spatial structures of objects in anomaly or normal regions are preserved. Meanwhile, the multi-granularity semantic integration block is devised to further integrate the global information of all granularities. Next, we use semantic-guided fusion blocks (SGFBs) to progressively upsample the features with the help of CAABs. Finally, the differences between the outputs of CAABs and SGFBs are calculated and merged to predict the anomaly defects. Thanks to the preservation of content consistency of objects, experimental results on two benchmark datasets demonstrate that our proposed MGCA-Net achieves superior anomaly detection performance over state-of-the-art methods.