Gas flow meters are important instruments for natural gas trade measurement, and changing of their health status can cause measurement deviation. To reduce economic loss for gas companies, this article proposes a gas flow meter health status assessment method based on multimodal data augmentation, morphological feature learning, and multiscale adaptive weighted morphological network. Firstly, data augmentation is performed by using the ACGAN algorithm based on Wasserstein distance and spectral normalization to achieve sample balance. Secondly, considering the complexity and noise impact of the gas flow meter vibration signal data, a morphological method based on average hat transform is proposed to extract the positive and negative pulse information from the signal. Finally, to address the non-stationary and variable operating conditions in industrial settings, a multiscale adaptive weighted morphological network is introduced. Multiple components with different structural element scales are used to extract pulse information, and adaptive weighted fusion is employed to enhance the scales that provide strong pulse components. The experimental results show that the proposed method has an accuracy of over 94% in the health status assessment of gas flow meters. This method has significant practical value for actual gas trade measurement.