In the non-contact respiratory state monitoring based on WiFi Channel State Information （CSI）， respiratory state data are easily affected by environmental interference during acquisition， resulting in degraded signal quality. Meanwhile， due to constrained acquisition conditions and privacy protection， the number of samples is often insufficient， leading to a few-shot problem， and the generated samples usually lack consistency with real samples in key discriminative features， thereby reducing the effectiveness of data augmentation. In addition， the small differences among different respiratory states further increase the difficulty of classification and limit the depth of research on multi-state respiratory recognition. To address these challenges， an SiamGAN-CNNBiLSTM-based respiratory state recognition system was proposed. First， a multi-state respiratory dataset consisting of normal breathing， apnea， and sneezing was constructed， and a systematic CSI signal preprocessing pipeline was designed to improve signal quality. Second， an SiamGAN module integrating Generative Adversarial Network （GAN） and Siamese network was developed， so that through the adversarial mechanism of generator and discriminator， samples with higher realism were generated. At the same time， a Siamese encoder was introduced to extract 128-dimensional intrinsic features of the signals， and a Mean Squared Error （MSE） constraint was imposed in the feature space to enhance the consistency between generated and real samples in key discriminative features. By this strategy， the discriminability and generalization of generated samples were strengthened， and the physical properties of the original signals were preserved， so as to avoid the introduction of classification-irrelevant high-frequency noise. Finally， a deep classification network combining Convolutional Neural Network （CNN） and Bidirectional Long Short-Term Memory （BiLSTM） network was constructed to fully mine the spatio-temporal features of respiratory signals， and achieve high-precision recognition of multiple respiratory states. Experimental results show that in the office scenario， SiamGAN-CNNBiLSTM achieves excellent performance with an accuracy improvement of 2.2 percentage points， compared with CNN-BiLSTM method， and an accuracy improvement of 8.3 percentage points， compared with the Transformer method； in corridor scenario， SiamGAN-CNNBiLSTM achieves an accuracy of 96.7%， representing a decrease of only 1.6 percentage points compared with that in office scenario， indicating the robustness of the proposed method across different environments.
