Shear-wave splitting is an important method to analyze seismic anisotropy. Some conventional methods use grid search to obtain splitting parameters,and then compare the measurement results of different methods for quality detection. This process consumes a lot of calculation time on poor quality data. In this paper,a new method for quality detection of shear wave splitting using deep neural networks is proposed for solving this problem. A deep neural network with a total of 9 convolutional layers based on the Resnet residual structure is constructed,which can directly classify the quality of two-component shear-wave waveform data. In the whole process,the neural network extracts the waveform features through the convolutional layer,calculates the loss function and then backpropagates to train model parameters. After the iterative training,the model could automatically classify the quality type by forward calculation for input waveform data. This paper uses synthetic data and actual data to train the network,and both can obtain accurate classification results. Compared with the results by some shear-wave splitting methods,the neural-network-based method can omit the calculation process of grid search and directly judge the quality type,which has obvious advantages in computing speed and can continue training to improve the accuracy of the model,and helps to improve the operational efficiency of the shear wave splitting method during data processing.