With the rapid development of the aviation industry, the safe flight of aircraft has become particularly important. A flight-level anomaly identification method based on unstable approach events during aircraft approach is presented to identify anomalous events in the aviation field. The method, called PVAE-WGAN, combines variational auto-encoders (VAE) and Wasserstein generative adversarial networks (WGAN), and uses a Pareto distribution to simulate the probability distribution of anomalous cases. The generators of VAE and WGAN are shared, and the hidden variables randomly sampled from the normal distribution and Pareto distribution are taken as input to the generator. The reconstructed output samples are taken as positive and negative samples, respectively. The Wasserstein distance is used as a measure between the distribution of positive and negative samples fitted by the model and the true distribution, so that both the generator and the discriminator gain the ability to distinguish anomalies, thereby achieving accurate detection of unstable approach events. The method was trained and tested using the real flight data recorder (FDR) data as an example, and it was found to be much better than existing multi-dimensional time series anomaly identification techniques that are appropriate for unstable approach detection. The F₁ score of the proposed method in this paper can reach 0.935, which is an average increase of 12.95% compared with other methods.