High-resolution flow field data has important applications in meteorology, aerospace engineering, high-energy physics and other fields. Experiments and numerical simulations are two main ways to obtain high-resolution flow field data, while the high experiment cost and computing resources for simulation hinder the specific analysis of flow field evolution. With the development of deep learning technology, convolutional neural networks are used to achieve high-resolution reconstruction of the flow field. In this paper, an ordinary convolutional neural network and a multi-time-path convolutional neural network are established for the ablative Rayleigh-Taylor instability. These two methods can reconstruct the high-resolution flow field in just a few seconds, and further greatly enrich the application of high-resolution reconstruction technology in fluid instability. Compared with the ordinary convolutional neural network, the multi-time-path convolutional neural network model has smaller error and can restore more details of the flow field. The influence of low-resolution flow field data obtained by the two pooling methods on the convolutional neural networks model is also discussed.