There are two major challenges in current research on pedestrian trajectory prediction: 1) how to effectively extract the spatial-temporal correlation between the front and back frames of pedestrians; 2) how to avoid performance degradation due to the influence of sampling bias in the trajectory sampling process. In response to the above two problems, a pedestrian trajectory prediction model was proposed based on the dual-attention spatial-temporal graph convolutional network and the purposive sampling network. Temporal attention was utilized to capture the correlation between the front and back frames, and spatial attention was utilized to capture the correlation between the surrounding pedestrians. Subsequently, the spatial-temporal correlations between pedestrians were further extracted by spatial-temporal graph convolution. Meanwhile, a learnable sampling network was introduced to resolve the problem of uneven distribution caused by random sampling. Extensive experiments showed that the accuracy of this method was comparable to that of the current state-of-the-art methods on the ETH and UCY datasets, but the number of model parameters and the inference time were reduced by 1.65×10⁴ and 0.147 s, respectively; while the accuracy on the SDD dataset slightly decreased, but the amount of model parameters was reduced by 3.46×10⁴, which showing a good performance balance. The proposed model can provide a new effective way for pedestrian trajectory prediction.