To extract the deep discrimination features from hyperspectral images， many labeled samples are often required； however， it is difficult to label samples in hyperspectral image. By using the characteristic of combining image with hyperspectral information， a semi-supervised dual path network （SSDPNet） based on deep-manifold learning was proposed. In this network， convolution and neural networks were used to extract the spatial-spectrum joint features from few labeled samples and many unlabeled samples， respectively. Then， the manifold reconstruction graph models based on supervised and unsupervised graphs were constructed to explore the manifold structure in hyperspectral images. In addition， a joint loss function based on mean square error and manifold learning was developed to jointly measure manifold boundary and spatial-spectral probability residuals to realize integrated feedback and optimize the dual path network； this results in land cover classification. The overall classification accuracies of experiments on WHU-Hi-Longkou and Heihe hyperspectral data sets reach 97.53% and 96.79% respectively， which effectively improves the ability to classify land covers.