The normal operation of flight control sensors is the key of the safety and stability of the aircraft. However, most of the anomaly detection methods only consider the correlation between sensors data or the correlation change of data between different sensors. When the operating conditions of aircraft change dynamically, the accuracy of anomaly detection results may be low and the false alarm rate is high due to insufficient feature extraction. In this article, a flight control sensor data anomaly detection method based on spatio-temporal correlation is proposed to achieve the fusion modeling of sensor data changes in time and space. Firstly, the feature extraction module of temporal evolution and spatial correlation is formulated to extract the features in time and space in parallel. Secondly, the spatiotemporal correlation fusion is carried out to obtain spatio-temporal correlation predictive data. Finally, based on the statistic of the residual between the predicted data and the actual data, the threshold is selected and the sensor data is detected. Through the verification of simulation and measurement data, compared with typical anomaly detection methods such as RVM, the anomaly detection accuracy rate of the proposed method is at least 0.4% higher and the false alarm rate is at least 1.8% lower.