Ladder networks is not only an effective deep learning-based feature extractor, but also can be applied on semi-supervised learning. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Autoencoders and restricted Boltzmann machines ignore the manifold information of high-dimensional data and usually achieve unmeaning features which are very difficult to use in the subsequent tasks, such as prediction and recognition. From the perspective of manifold learning, a novel deep representation method Laplacian ladder networks (LLN) is proposed, which is based on ladder networks (LN). When training LLN, LLN reconstructs noisy input and encoder layers, and adds graph Laplacian constrains to learn hierarchical representations for improving the robustness and discrimination of system. Under the condition of limited labeled data, LLN fuses the supervised learning and unsupervised learning to training in a semi-supervised manner. This study performs the experiments on the MNIST and CIFAR-10 datasets. Experimental results show that the proposed method LLN achieves superior performance compared with LN and other semi-supervised methods, and it is an effective semi-supervised method.