Sign language recognition is widely used in communication between deaf-mute and ordinary people. In adequate extraction of spatial-temporal features in sign language recognition task is likely to result in low recognition rate. In this paper, proposed is a novel sign language recognition model based on spatial-temporal attention which can learn more discriminative spatial-temporal features. Specially, a new spatial attention module based on residual 3D convolutional neural network (Res3DCNN) is proposed, which automatically focus on the salient areas in the spatial region. Then, to measure the importance of video frames, a new temporal attention module based on convolutional long short-term memory (ConvLSTM) is introduced. The crucial purpose of the proposed model is to focus on the salient areas spatially and pay attention to the key video frames temporally. Lastly, experimental results demonstrate the efficiency of the proposed method on the Chinese sign language (CSL) dataset.