： Due to its ability to reveal the intrinsic molecule specificity of DNA/RNA at subcellular lateral resolution, photoacoustic (PA) microscopy holds great promises in histopathology imaging of tissue samples. An essential marker for subsequent illness study and diagnosis is the histopathological picture. Segmenting the histopathological image of cell nuclei has been significantly aided by contemporary image processing technology, while they usually suffer from inadequate segmentation or training resource waste. To address this challenge, we propose an approach, called Classes U-Net, which combines the information entropy classification with U-Net and U-Net++ architecture for the segmentation of photoacoustic histology image. The results show that our Classes U-Net effectively improves the DICE to 91.43%, IOU to 84.215, better than U-Net’s 83.28% and 71.35%, better than U-Net++’s 84.60% and 73.31%, and our Classes U-Net reduce the required computing resources.