Over the years, microservices have become increasingly popular and are being adopted more and more, microservice architecture is gradually replacing monolithic applications as the mainstream architecture. Anomaly detection for microservices is a hot topic of current research, which is important to ensure the security and performance of the application. This paper proposes a deep learning based anomaly detection method for microservices, which first serializes trace data, then trains a model combining AE and LSTM to detect anomalies, and also detects performance anomalies by executing time series. We evaluated our approach on a widely adopted microservice system and showed that our approach outperformed current trace-based approaches.