3D modeling accurately depicts the physical world but typically requires substantial data acquisition resources and significant storage space. We introduce a novel three-dimensional slice-reconstruction model (3DSR) to address these challenges. This 3D data super-resolution model leverages low-resolution 3D data as input to generate high-resolution results promptly and accurately, reducing the time and storage required to create detailed 3D models. To enhance the computational efficiency and accuracy of deep learning models, the 3D data are partitioned into multiple slices. The 3DSR processes each slice into a high-resolution 2D image, which is then reassembled into high-resolution 3D data. Our slice-up method and slice-reconstruction technique are specifically designed to preserve the primary characteristics of the 3D data. We employ a pre-trained deep 2D convolutional neural network to expand the resolution of the 2D image, resulting in excellent performance. This approach reduces the time required for training deep learning models and enhances computational efficiency during the resolution improvement process. Importantly, our model can deliver superior performance even when trained on fewer data.