Precise liver segmentation is crucial for the localization and treatment of liver cancer, and in view of the problems of different liver shapes and sizes, as well as the difficulty of segmentation of edges and lesion areas, a liver segmentation network based on multilayer sensor and multi-scale feature extraction (M2U-Net) is proposed. The network is divided into convolutional phases and multilayer perceptron phases. First, an extrusion excitation module is added to the encoder portion of the convolution phase to highlight specific liver segmentation tasks and inhibit irrelevant background areas. Secondly, a tokenized multilayer perceptron module is added to the multilayer perceptron stage to reduce the complexity of the model. The transition layer adds a multi-scale feature extraction module to adapt to the segmentation of livers at different scales and the segmentation of detail areas. Finally, experimental results on the LiTS dataset and the dataset provided by Oriental Hepatobiliary Hospital show that the segmentation network is better than the segmentation networks such as U-Net, U-Net++ and CE-Net on three evaluation indicators.