In complex and ever-changing electromagnetic environments, a large number of anomalous radio signals illegally occupy spectrum resources and interfere with legitimate communications. To address this challenge, this paper proposes an anomalous radio signal detection method based on an Adversarial Autoencoder (AAE). The method comprises an autoencoder and a Generative Adversarial Network. The autoencoder is used to extract time–frequency features of radio signals, generate latent feature vectors, and reconstruct the signals. By comparing the original signals to their reconstructions, anomalies can be rapidly and accurately detected via the reconstruction error. Meanwhile, the adversarial network regularizes the latent vectors produced by the encoder, forcing them to approximate a predefined prior distribution, thereby improving the model’s generative capability and enhancing the structural consistency of the latent representations. We used a Pluto SDR device to capture real-world radio data in the field, performed frequency-domain analysis, and constructed a high-resolution power spectrum time–frequency dataset for model training and testing. Experimental results show that the proposed method achieves a detection accuracy of 95.5% for anomalous radio signals, exhibiting excellent performance on multiple metrics across diverse scenarios.