To solve the problems that the existing software system abnormal status evaluation methods over depend on data labeling and pay less attention to the time dependence of time-series data, and then it is difficult to quantify the software system abnormal status. Thus, a software system abnormal status evaluation method based on the hybrid generative network is proposed. Firstly, by combining the long short-term memory network (LSTM) and the variational auto-encoder (VAE), an anomaly detection model based on LSTM-VAE hybrid generative network is designed. The features of the system time-series data are extracted by LSTM and its distribution is modeled by VAE. Then, the LSTM-VAE anomaly detection model detects the software system key feature parameters and obtains the anomaly metric value of system key feature parameters. Finally, the coupling degree method is used to optimize the linear weighted sum method. According to the weighted coupling degree method which is optimized, the software system abnormal status quantitative value is calculated, and the software system abnormal status is evaluated. The experimental results show that the proposed model has a better detection ability for the abnormal time-series data of the software system, and its system abnormal status evaluation result is more feasible and effective.