The use of X-ray Computed Tomography (CT) leads to the concern of lifetime cancer risk. Low-dose CT scan with reduced mAs can reduce the radiation exposure, but the image quality is usually degraded due to excessive image noise. Numerous studies have been conducted to regularize CT image during reconstruction for better image quality. In this paper, we propose a fully data-driven manifold learning approach. An auto-encoder-decoder convolutional neural network is established to map an entire CT image to the inherent low-dimensional manifold, and then to restore the CT image from its manifold representation. A novel reconstruction algorithm assisted by the leant manifold prior is developed to achieve high quality low-dose CT reconstruction. We perform comprehensive simulation studies using patient abdomen CT images. The trained network is capable of restoring high-quality CT images with average error of ∼ 20 HU. The manifold prior assisted reconstruction scheme achieves high-quality low-dose CT reconstruction, with average reconstruction error of ∼ 38.5 HU, 4.6 times and 3 times lower than that of filtered back projection method and total-variation based iterative reconstruction method, respectively.