Abstract: Efficiently and accurately predicting the evolution of hypervelocity impact debris is crucial in the design of spacecraft protective structures. To this end a deep learning model is constructed. The model is based on the conditional variation auto encoder (CVAE) and massive smoothed particle hydrodynamics (SPH) simulations. The model includes four controllable labels, namely projectile velocity (3.00-8.00 km/s) radius (2.00-8.00 mm), target plate thickness (1.000-4.000 mm), and time instant (1.0-12.0 μs). The model uses 200 parameters to describe the debris mass distribution, resulting in an average error less than 0.6%. It takes less than 7 micro seconds to predict one debris mass distribution.