As China′s space industry advances from being a space power to a space strong nation, the number and density of spacecraft launches have reached new heights. Ensuring the normal operation of spacecraft in orbit has become a crucial task. Spacecraft telemetry data is an important basis for ground control to determine normal operation, and enhancing the anomaly detection capability of telemetry data is key to improving ground control′s support capabilities. Currently, anomaly detection of telemetry data mainly relies on expert experience and fixed thresholds. While these methods are efficient and reliable, they struggle to cope with the complex and dynamic operating environment in orbit, and the detection accuracy still needs improvement. Traditional machine learning methods show limited performance and effectiveness as the volume of telemetry data increases. In recent years, deep learning methods have shown great potential in the field of anomaly detection. However, existing deep learning-based anomaly detection methods for spacecraft telemetry data still face significant challenges. On the one hand, they heavily rely on the accuracy and completeness of anomaly labels, while obtaining a large amount of accurate anomaly-labeled data in practical engineering is difficult. On the other hand, existing methods lack the ability for online anomaly detection, which is essential for meeting the real-time monitoring needs of spacecraft in orbit. To address these issues, this paper proposes an online and unsupervised anomaly detection model, Feen-LSTM. This model extracts global spatiotemporal features from multidimensional telemetry data using a Transformer structure and combines LSTM to model local temporal dependencies, thereby achieving an optimized structure for feature enhancement. Experiments on two spacecraft telemetry data sets published by NASA show that Feen-LSTM can effectively improve the accuracy of anomaly detection, especially in the face of complex data and unknown anomaly patterns, and show better performance than other methods.