Abstract: The flow field PIV measurement method cost a lot, but the measurement results have low spatial and temporal resolution. The spatio-temporal reconstruction method of flow field based on experimental and numerical simulation data is studied. In order to realize the high-resolution spatio-temporal reconstruction of the experimentally measured low-resolution data, a flow field spatio-temporal reconstruction method based on deep neural network is proposed. A hybrid deep neural network based on convolutional neural network and long-short-term memory neural network is constructed. This hybrid deep neural network is trained to learn the spatio-temporal evolution features of the flow field. After the training is completed, it can be used to reconstruct the experimental data into spatio-temporal high-resolution results. The test results show that when the spatial high-resolution reconstruction is performed alone, the mean square error between the reconstructed flow field and the ground truth flow field is about 0.0065, and the number of data points is 51 times more than that of the input field. When the flow field is reconstructed to high resolution in time and space at the same time, the mean square error be maintained at about 0.065, and the density in the time dimension is 5 times more than that of the input field. It is proved that this method can greatly improve the efficiency of the experiment and save the cost of the experiment.