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Bearing fault diagnosis is of great significance for reducing the risk of damage to rotating machinery and further improving economic benefits. Deep learning has been widely used in bearing fault diagnosis, but deep learning models are prone to performance degradation due to noise interference during training and testing. Moreover, the operating conditions of bearings change frequently, making it difficult to collect data under different conditions. To address this issue, this paper proposes a bearing fault diagnosis method based on transfer QCNN (Quadratic Convolutional Neural Network) and Siamese network. The QCNN is first pre-trained to obtain model parameters with strong discriminative power. Then, the pre-trained parameters are transferred to the QCNN used as a sub-network in the Siamese network. The Siamese network is then trained to obtain the model. Finally, the test data and fault data are combined to form data pairs input to the model, and the fault type of the test data can be obtained. This method combines QCNN with Siamese network, where the Quadratic neurons in QCNN have powerful feature extraction capabilities, and the Siamese network is trained with shared weights and relative relationships, which helps alleviate the impact of noise and imbalanced operating condition data. Experimental results show that compared to traditional machine learning models and QCNN, the proposed method performs better in dealing with noise and imbalanced operating condition data.