Abstract: To address the high computational cost of traditional ballistic prediction methods and their difficulty in meeting rapid assessment needs, this paper proposes an efficient predictive model based on a Convolutional Neural Network (CNN) for the penetration ballistics of multi-layer thin concrete targets. First, using a numerically simulated method validated through experiments, the significant influence of projectile angular velocity on trajectory deflection was analyzed and confirmed. This parameter was subsequently identified as a key projectile-target engagement condition. By systematically adjusting initial parameters, a dataset containing 127 cases of single-layer thin concrete target penetration was constructed. Building on this, a high-precision ballistic prediction model for single-layer target penetration was developed, with projectile parameters, target parameters, and engagement conditions as inputs, and post-impact projectile motion parameters as outputs. Furthermore, by integrating rigid-body kinematic equations describing the projectile's flight between targets, a complete penetration-flight iterative prediction framework was established, enabling rapid prediction of ballistic characteristics for multi-layer spaced thin concrete targets. The results indicate that an increase in counterclockwise angular velocity leads to a positive rise in radial residual velocity behind the target and upward trajectory deflection, while clockwise angular velocity produces the opposite effect. These findings clearly demonstrate that projectile angular velocity is a critical and non-negligible parameter in thin-target penetration. For single-layer target scenarios, the model demonstrated strong predictive performance, with mean MSE values for the training and test sets stabilizing around 0.0012 and 0.0019, respectively. In multi-layer target predictions, while maintaining accuracy (maximum relative error in residual velocity of 10.65% and maximum absolute error in attitude angle of 3.47°), the model's computation time was only 0.05% of that required by traditional numerical simulation methods. This study not only reveals the influence of the key factor—projectile angular velocity—on penetration ballistics but also offers a novel modeling paradigm of "data-driven + physics-equation fusion," providing an important methodological reference for weapon damage effectiveness assessment and design optimization.