Uncertain spatial information is widely used in many scientific fields. However, the current methods for uncertain spatial information reconstruction need to scan the Training Image (TI) for many times, and then obtain the simulation results through complex probability calculation, which leads to the low efficiency and complex simulation process. To address this issue, a method of Fisher information and Variational Auto-Encoder (VAE) jointly applying to the reconstruction of uncertain spatial information was proposed. Firstly, the structural features of the spatial information were learned through the encoder neural network, and the mean and variance of the spatial information were obtained by training. Then, the random sampling was carried out to reconstruct the intermediate results according to the mean and variance of the sampling results and the spatial information, and the encoder neural network was optimized by combining the optimization function of the network with the Fisher information. Finally, the intermediate results were input into the decoder neural network to decode and reconstruct the spatial information, and the optimization function of the decoder was combined with the Fisher information to optimize the reconstruction results. By comparing the reconstruction results of different methods and the training data on multiple-point connectivity curve, variogram, pore distribution and porosity, it is shown that the reconstruction quality of the proposed method is better than those of other methods. In specific, the average porosity of the reconstruction results of the proposed method is 0.171 5, which is closer to the 0.170 5 porosity of the training data compared to those of other methods. Compared with the traditional method, this method has the average CPU utilization reduced from 90% to 25%, and the average memory consumption reduced by 50%, which indicates that the reconstruction efficiency of this method is higher. Through the comparison of reconstruction quality and reconstruction efficiency, the effectiveness of this method is illustrated.