Obstructive sleep apnea (OSA) is a prevalent sleep disorder that seriously affects patients’ quality of life and health status. Traditional diagnostic methods are time-consuming and labor-intensive, and apnea detection using deep learning algorithms also faces the problems of insufficient sample size and class imbalance. Therefore, in this paper, we propose a semisupervised apnea detection algorithm (Semi-DynaSeqNet) based on the oxygen saturation (SpO2) signal. In this study, we first extracted local features of the SpO2 signal using a one-dimensional convolutional neural network, and then combined the gate recurrent unit for time series modeling to capture the signal’s long-term dynamic features. On this basis, a self-attention mechanism is introduced to further enhance the recognition of key features. Considering the small-sample classification task of the OSA detection, we further proposed the semisupervised learning method with the adaptive adjustment of threshold. By iteratively training a model to generate pseudo-labeled samples of unlabeled pulse oximetry signals and incorporating them into the training set, while adaptively adjusting the semisupervised threshold to fully utilize the unlabeled sample information, we thereby improved the generalization ability of Semi-DynaSeqNet. The experimental results showed that the algorithm proposed in this paper achieves an F1-score of 90.94% for the model on the St. Vincent’s University Hospital/University College Dublin Sleep Apnea Database with one-second detection, whereas the algorithm achieves an F1-score of 94.65% on the PhysioNet Apnea-ECG Database with one-minute detection, indicating that the algorithm can perform well in both sleep apnea detection tasks with different time scales, demonstrating its flexibility and scalability.