Due to the complexity and dynamics of the semiconductor manufacturing processes, wafer maps will present various defect patterns caused by various process faults. Identification of wafer map defect patterns can help operators in finding out root-causes of abnormal processes, and then ensures that the manufacturing process is restored to the normal state as soon as possible. This paper proposes a wafer map defect recognition (WMDR) model based on integration of deep transfer learning. Our model reduces the training time and improves feature learning performance of DenseNet. In addition, the recognition algorithm based on transfer learning can solve the problem of class imbalance in the WMDR task.