<p>The underwater launch of an axisymmetric body involves complex cavity-structure interactions. Studying the evolution of cavity pressure around an axisymmetric body is crucial for researching its motion stability. In this work, we propose a deep neural network model for cavity pressure distribution recovery, called CPDR-net. This model can reconstruct the full-domain distribution of surface pressure based solely on the local pressure distribution. The CPDR-net model was trained using numerical simulation data with different launch depths and initial velocities, and subsequently tested on two simulation datasets under new conditions. Both training and testing datasets are obtained from the ventilated cavitating flow over an underwater axisymmetric vehicle. Results demonstrated that CPDR-net can accurately predict the pressure distribution along each longitudinal line of the axisymmetric body and provide the pressure evolution over time for each point on the surface. Thus, we can obtain the evolution of surface pressure distribution throughout the entire voyage process based on the CPDR-net model. The findings from this study may provide a valuable reference for subsequent research on underwater launches.</p>