In order to solve the problem that most of the existing deep neural ne tworks for classification are flat network architectures and seldom pay attention to the hi erarchical structure of data categories,which leads to the difficulty of classifier training,a multi- task learning classification network model based on hierarchical relationship of data is proposed.Relying on tomato leaf disease in the hierarchical structure of the information design with a Shared network co mposed of coarse- grained to fine-grained hierarchy for the degree of disease classification,a n etwork model with ResNet-50as the backbone network,including two sub networks:coarse-graine d network module is responsible for the distinction between tomato diseases,a total of five clas ses,fine-grained network module in the residual network module on the basis of adding SE module i s responsible for the degree of disease classification,a total of 9class.Through the verific ation of each branch of the network architecture and the comparison with the three flat networks of VGG -16,ResNet-34and ResNet-50in the classification task of disease degree,the feasibility and effectiveness of the network structure in this paper are proved,and the classification accuracy of t he final test set reaches 93.97%.It is proved that the multi-task classification method combined with the hierarchical structure of data and network in this paper is an effective algorithm for disea se degree classification.