Aiming at the problem that the existing hyperspectral image classification algorithm based on ladder network (LN) cannot fully extract the spatial-spectral features of the image, which leads to the reduction of classification accuracy, a hyperspectral semi-supervised classification algorithm based on improved ladder network is proposed. First, the three-dimensional convolutional neural network (3D-CNN) and the long-short-term memory (LSTM) network are combined to propose a new spatial-spectral feature extraction (3D-CNN-LSTM) network, which is used to extract local spatial features step by step. Then, the 3D-CNN-LSTM network is used to improve the encoder and decoder of the ladder network, and a 3D-CNN-LSTM-LN semi-supervised classification algorithm is proposed to enhance the feature extraction ability of the ladder network. Finally, different algorithms are tested on Pavia University and Indian Pines datasets. The experimental results show that the proposed algorithm achieves the best classification effect under the condition of small samples, which verifies the superiority of the proposed algorithm.