A fault diagnosis migration method based on CNN-FT (convolutional neural network with fine-tuning)is proposed, which leverages the informative prior knowledge from the source-domain variable refrigerant flow system to establish a diagnostic model for the target variable refrigerant flow system. Firstly, the source-domain is pre-trained, and the optimal CNN model is found by parameter optimization. Then the pre-training model is migrated to the target domain, and only a small amount of target data is used to train the top layer of CNN, with an accuracy of 86.71%. The previous network layer is thawed in turn for fine-tuning, and the accuracy rate is improved to 95.83%, which is significantly better than the target domain specific training (81.02%) and the source domain model direct migration (33.45%).