Abstract: To enhance the accuracy of hyperspectral object recognition, a hyperspectral image object recognition model based on the self-ensembling network is proposed. By introducing a regularization term to optimize the self-ensemble network, this model improves the generalization performance of the object recognition model and builds a self-ensemble learning mechanism to address the underfitting problem under limited labeled samples, reducing the dependence of the hyperspectral image recognition model training on a large number of labeled samples. The model consists of a student network and a teacher network, with a dense connection module with gradient operators added to the network to enhance the network's perception of edge and fine-grained features and improve the feature extraction performance of hyperspectral images. Under the joint constraints of supervised and unsupervised losses, the student network and the teacher network learn from each other, thereby establishing the model's self-ensemble mechanism and ensuring the model's classification accuracy. To further enhance the model's generalization performance, an L₂ regularization term is introduced during model optimization to constrain the training and optimization of the objective function, thereby overcoming the overfitting problem of the model. The proposed method is applied to three hyperspectral datasets, Pavia University, Salinas, and WHU-Hi-LongKou, with average classification accuracies of 96.91%, 96.73%, and 98.12%, respectively. Compared with multiple classification algorithms, it is verified that the proposed method has better classification accuracy under limited labeled samples.