A multi-scale hybrid attention capsule network (CapsNet) model is proposed to solve the problem of insufficient feature extraction due to single feature extraction structure in CapsNet and excessive amount of parameters in data processing.First,convolution kernels of different scales are added at the initial end of the network to extract features at multiple angles,and channel attention (CA) mechanism and spatial attention (SA) mechanism are introduced to reduce complex background interference by focusing on features of more resolved regions.Second,a local pruning algorithm is adopted to optimize the dynamic routing algorithm,which reduces calculation parameters and training time.Finally,validation on open marine fish data set F4K (Fish 4 Knowledge) shows that the model recognition accuracy in this paper is 98.65% compared with traditional residual network50 (ResNet-50),bilinear convolutional neural network (B-CNN),spatial transformation network and hierarchical compact bilinear pooling (STN-H-CBP) and CapsNet models,5.92% higher than ResNet-50 model;The training time is 2.2 h,which is nearly 40 min shorter than that of CapsNet,which verifies the feasibility of the proposed algorithm.