In recent years, intelligent fault diagnosis methods based on deep learning have been widely used in the wind power industry. These methods mostly rely on sufficient labeled data to support model training. However, new wind farms often face the challenge of insufficient labeled data, which will hinder the practical application of the deep learning methods. In addition, data distributions of different wind farms are usually different, so the model trained by other wind farm data cannot be directly used for fault diagnosis of new wind farms. In view of the lack of labeled data in new wind farms, a transfer learning method: weighted joint matching adaptive network (wJMAN) is proposed. This method realizes the alignment of the source domain (other existing wind farms) and the target domain (the new wind farm) through sub-domain matching and joint distribution adaptation. Therefore, the information of other wind farms can be more effectively used in the construction of the fault diagnosis model of the new wind farm. In addition, in view of the imbalance between classification loss and adaptive loss in existing domain adaptation methods, an adaptive loss weight method is proposed, which makes the classification loss and adaptive loss more balanced and converges faster in the optimization process. The proposed wJMAN method is tested with the operating data from 4 real wind farms. The results show that wJMAN has higher diagnostic accuracy than common transfer learning methods.