To the problems of low computational efficiency, strong grid dependence, and insufficient real-time performance in the traditional computational fluid dynamics (CFD) method for flowmeter performance analysis, deep learning technology was introduced to construct a flowmeter performance prediction method based on a spatiotemporal sequence model PredRNN_V2. In the task of predicting the internal flow field of a unilateral Venturi flowmeter, the iterative flow field evolution during the steady-state CFD solution process was modeled as a spatiotemporal sequence prediction problem, and a complete process from data acquisition, preprocessing to model training and evaluation was constructed. ConvLSTM, PredRNN, and PredRNN_V2 were selected for comparison, and mean squared error (MSE), structural similarity index (SSIM), learned perceptual image patch similarity (LPIPS), and peak signal-to-noise ratio (PSNR) were used as evaluation indicators to quantitatively assess the prediction performance. The results showed that the MSE output by the PredRNN_V2 model increased the most slowly, with an average PSNR of 36.8, significantly higher than PredRNN and ConvLSTM. The average SSIM of PredRNN_V2 was 0.92, and the LPIPS was 0.069, indicating that its prediction results had lower noise and clearer cloud boundary, and its prediction accuracy was significantly superior to the other two models while maintaining a high computational efficiency. The relative error between the prediction results of the PredRNN_V2 model and the simulation results was within ±5%, indicating the reliability and accuracy of the model in predicting the flow field.