Abstract: Anomaly detection for wind tunnel balance force data is beneficial to analyze anomaly reasons, improve test schema and troubleshoot equipment problems. To solve the high time cost and low-efficiency problems of the manual detection method, a deep learning-based anomaly detection method is proposed. To solve the problem of no abnormal data, we summarize the most common abnormal types in the wind tunnel test. For the problem that the dimensions of data in different experiments are different, a standardization scheme based on statistical characteristics is proposed. Finally, a deep learning model is utilized to learn abnormal features and detect abnormal data. Experimental results show that our deep learning-based anomaly detection method can achieve 81.7% accuracy and 72.6% recall, and has a good detection performance for isolated jump points and multipoint anomalies.