There exists serveral challenges in MRI images of bladder cancer, such as unclear tumor boundaries, small tumor areas, and discontinuous tumor distribution.The existing segmentation algorithms have huge parameters and complex calculations, and the accuracy and efficiency of the existing methods for bladder tumor segmentation need to be improved. In response to the above issues, a lightweight bladder cancer segmentation algorithm based on multi-scale feature fusion (PylNet) was proposed in this paper. The algorithm can extract information of different scales through the multi-scale semantic feature extraction module. This module was designed in the coding stage to ensure the reliability and comprehensiveness of the extraction of information on small tumor regions. At the same time, the fusion module designed in another stage can quickly complete the tumor region segmentation, and the amount of parameters used is much less. Experimental results show that compared with FCN8s, SegNet, U-Net and other algorithms, the segmentation accuracy of this algorithm is improved to a certain extent where DSC reaches 88.40%, and the parameter amount is 1/13 of FCN8s, which a fast bladder MRI segmentation is achieved.