Stent target segmentation in intravascular optical coherence tomography (IVOCT) images are easily affected by complex backgrounds, leading to unsatisfactory segmentation results. To solve this issue, this study proposes a multiscale fusion attention mechanism stent segmentation network (FAU-Net) that uses U-Net as the primary part of the network structure. First, the wavelet transform downsampling module is used instead of the original pooling layer to fully retain the edge and detail information. Subsequently, a multidimensional fusion attention module is embedded in the encoder block to enhance the model's ability of detecting small target stents. Finally, a multiscale convolution module is designed at the jump connection to help the network localize the target using multiscale features, simultaneously improving the sensitivity of the network to the target surroundings. The proposed FAU-Net is evaluated on a dataset consisting of 5622 optical coherence tomography (OCT) images, and the Dice coefficient reaches 75.91%, which is 4.00 percentage points better than that of U-Net, demonstrating an improved performance.