The test section’s Mach number in wind tunnel testing is a significant metric for evaluating system performance. The quality of the flow field in the wind tunnel is contingent upon the system's capacity to maintain stability across various working conditions. The process flow in wind tunnel testing is inherently complex, resulting in a system characterized by nonlinearity, time lag, and multiple working conditions. To implement the predictive control algorithm, a precise Mach number prediction model must be created. Therefore, this report studies the method for Mach number prediction modelling in wind tunnel flow fields with various working conditions. Firstly, this paper introduces a continuous transonic wind tunnel. The key physical quantities affecting the flow field of the wind tunnel are determined by analyzing its structure and blowing process. Secondly, considering the nonlinear and time-lag characteristics of the wind tunnel system, a CNN-LSTM model is employed to establish the Mach number prediction model by combining the 1D-CNN algorithm with the LSTM model, which has long and short-term memory functions. Then, the attention mechanism is incorporated into the CNN-LSTM predic-tion model to enable the model to focus more on data with greater information importance, thereby enhancing the model's training effectiveness. The application results ultimately demonstrate the efficacy of the proposed approach.