Aiming at the problems such as low estimation accuracy and poor robustness of traditional static soft measurement model in wind tunnel flow measurement， an Attention-LSTM-Kalman measurement model combing attention mechanism （Attention）， long short-term memory （LSTM） and Kalman filtering （Kalman） is proposed： a static soft-measuring model is established through LSTM network. On this basis， an improved scheme based on attention mechanism is proposed. Considering the dynamic characteristics of the system， Kalman filter is used to dynamically adjust the output sequence of the soft-measuring model. Experimental results show that LSTM is better than recurrent neural network （RNN） and gated recurrent unit （GRU） models. The comparison of the prediction results of the three models based on LSTM， Attention-LSTM and Attention-LSTM-Kalman shows that the attention mechanism could effectively improve the accuracy of the model， and the introduction of Kalman filter improves the dynamic measurement characteristics of the model. The feasibility and effectiveness of the proposed model are verified by the flow measurement in the wind tunnel system.