An improved 3D UNet network model that combines a dual path attention (DPA) module and a multi-scale feature aggregation (MFA) module is proposed to address the problems such as blurred edges,low contrast,and uneven gray value distribution in magnetic resonance (MR) images of the prostate bringing about the poor segmentation accuracy.Firstly,the dataset is resampled and cropped to fit the model input.Then,DPA and residual connection are added to each layer of the 3D UNet network encoder to enhance the feature coding capability.At the same time,an MFA module is added to the network decoder to make full use of spatial context information and enhance semantic information.Finally,the proposed model is validated on the public dataset PROMISE12,with the Dice coefficient of 89.90% and the Hausdorff distance of 9.37 mm. Compared with other models,the proposed model has better segmentation results,and the number of parameters and the amount of computation are less.