<p>Deep learning is a rapidly growing field that can effectively extract latent features from data and use them to make predictions based on the learned features, but most models just sum the loss of each sample without considering the relationship between samples. On the other hand, the traditional Laplacian Support Vector Machine (LapSVM) can effectively utilize samples and the relationship between samples by constructing a Laplacian graph, and performs well on semi-supervised data. In this paper, we combine LapSVM and deep learning and propose Deep Laplacian Support Vector Machine. Our approach is to first use a Deep Neural Network to extract the latent features from the image, then based on the extracted feature information and a small amount of original label information, we use LapSVM for classification, build a loss function, and finally iteratively update the two parts together. We evaluate our method on several benchmark datasets and demonstrate that it outperforms other semi-supervised learning methods.</p>