Abstract: A transfer learning-based classification method for aircraft harness template drawing content was designed for the recognition and classification of Electrical Wiring Interconnection Systems(EWIS) harness template drawings during change review. The harness template drawing element dataset was established from the existing harness template drawings, the VGG-16 network was selected as the basic network to optimize the model structure and parameters. To solve the problem of small dataset size and to improve the recognition accuracy and training efficiency of the model, the pre-trained model trained in the large-scale dataset was migrated to the harness template drawing element dataset for training, and the effects of different migration learning methods and different network structures on the model performance were compared. The experimental results showed that the fine-tuning transfer learning model had a high classification accuracy of 98.89%. Moreover, compared with other networks, the fine-tuning transfer learning model had a shorter training time and higher recognition accuracy, which has better application prospects.