<p>The inducer is a key component of aerospace centrifugal pumps, and its performance directly affects cavitation characteristics and overall efficiency of the pump. Based on the theory of reverse engineering, a parametric intelligent optimization (PIO) platform for the inducer was constructed, achieving precise extraction of geometric parameters, numerical simulation, and automatic optimization design. The head of the inducer was chosen as a constraint; the maximum hydraulic efficiency and the minimum critical net positive suction head (NPSH_<i>r</i>) were chosen as the objectives, and the Latin hypercube sampling method and the Particle Swarm Optimization algorithm were used to perform multi-objective optimization on the inducer. The results demonstrate that the proposed reverse reconstruction and optimization framework achieves high predictive accuracy, with relative errors of only 1.54% in head and 1.12% in NPSH_<i>r</i> when compared with experimental data, confirming the reliability of the platform. More importantly, the optimized inducer exhibits a significant performance improvement, with hydraulic efficiency increased by 5.75 percentage points and NPSH_<i>r</i> reduced by 23.89%. In addition, the low-pressure region in the hub area is effectively suppressed, and the flow field uniformity is improved, leading to an increase in cavitation inception margin.</p> Graphical abstract <p></p>