Aiming at the problems of low detection accuracy and poor generalization ability of Wreless Sensor Network （WSN） intrusion detection methods on imbalanced datasets with discrete high-dimensional features， an intrusion detection method for WSN based on Bidirectional Circulation Generative Adversarial Network was proposed， namely BiCirGAN. Firstly， Adversarially Learned Anomaly Detection （ALAD） was introduced to improve the understandability of the original features by reasonably representing the high-dimensional， discrete original features through the latent space. Secondly， the bidirectional circulation adversarial structure was adopted to ensure the consistency of bidirectional circulation in real space and latent space， thereby ensuring the stability of Generative Adversarial Network （GAN） training and improving performance of anomaly detection. At the same time， Wasserstein distance and spectral normalization optimization methods were introduced to improve the objective function of GAN to further solve the problems of mode collapse of GAN and lack of diversity of generators. Finally， because the statistical properties of intrusion attack data changed in an unpredictable way over time， a full connection layer network with Dropout operation was established to optimize the anomaly detection results. Experimental results on KDD99， UNSW-NB15 and WSN_DS datasets show that compared to Anomaly detection with GAN （AnoGAN）， Bidirectional GAN （BiGAN）， Multivariate Anomaly Detection with GAN （MAD-GAN） and ALAD methods， BiCirGAN has a 3.9% to 33.0% improvement in detection accuracy， and the average inference speed is 4.67 times faster than that of ALAD method.