Abstract: Tomato diseases and pests are leading factors to cause decline of tomato production． Identifying types of diseases and pests accurately is one of current international hot issues, which helps to take timely and effective measures to control diseases and pests, and reduce and avoid economic losses caused by decline of tomato production． Aiming at problems of low efficiency and accuracy of traditional diseaese and pests identification methods, tomato data set on Kaggle website were used to build a deep residual network model（ResNet）based on Squeeze-and-Excitation（SE）module to optimize tomato pests diseases and identification method． Results showed that: through transfer learning under Pytorch framework, average recognition accuracy of improved network model for tomato pests and diseases images was as high as 97.96%; ResNet network model based on SE module helped to enhance ability of distinguishing features, which increased universality and robustness of the model． Results of this study were of great significance for timely monitoring and treatment of tomato diseases and pests and increase of tomato production．