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This paper presents a health assessment method for wind tunnel based on deep learning networks, which utilizes the LSTM encoder-decoder to build up the normal status features space via computing the hidden status of examples data. Meanwhile, the quantitative value of the diversity between the measurement data and example data is calculated using Euclidean distance from feature vector to the normal status feature space, and then hundred-mark health index can be specified. As result, health assessment method achieves high accuracy in evaluation of two fault data sets of wind tunnel, which show a significant effect and strong application value.