In the paper, we propose a domain adaptation (DA) scheme based on the hybrid classical-quantum neural network, named hybrid DA. There are four parts, including the classical convolutional neural network part, the label predictor part based on quantum neural network (QNN), the domain classifier part based on QNN, and the gradient reversal layer part. The features from high-dimensional images are extracted by the feature extractor part and then are encoded in the following quantum predictor part and quantum classifier part. After the classical and quantum neural networks are trained jointly, those features that cannot be discriminated between the source and the target domain can be effectively predicted by the quantum predictor. The performance of the proposed DA is verified over DIGIT-5 dataset. The simulation results demonstrate the feasibility of the proposed hybrid DA and show that the hybrid DA has a higher classification accuracy with fewer parameters. It can provide a promising DA in the current era of noisy intermediate-scale quantum devices. [ABSTRACT FROM AUTHOR]