To solve the problems of small number of anomaly samples and inefficient feedback of anomalies in the anomaly detection tasks of large-scale software system, a log-level anomaly detection algorithm based on Generative Adversarial Network (GAN) and attention mechanism. First, the unstructured logs were converted into structured events through the log templates, and each event included timestamps, signature and parameters. Second, through sliding window method, the sequence of the parsed events were divided into patterns, and the real training dataset was comprised combination of the divided event patterns and the corresponding following events. Third, the real event patterns were used as the training samples to train the attention mechanism-based GAN, and the Recurrent Neural Network (RNN) based generator was trained through the adversarial learning mechanism until it converged. Finally, through the input flow event pattern, the generator generated the possibility distribution of normal and abnormal events based on the previous pattern. When the threshold was set, whether the specific log of next moment is a normal event or an abnormal event was determined automatically. Experimental results show that the proposed anomaly detection algorithm, which uses a gated recurrent unit network as the attention weight and a Long Short-Term Memory (LSTM) network to fit event patterns, has a 21.7% increase in precision compared to the algorithm only using the gated recurrent unit network. In addition, compared to the log-level anomaly detection algorithm LogGAN, the proposed algorithm improves the precision of anomaly detection by 7.8% over the performance of LogGAN.