In this paper, a nanosecond pulsed laser was used for laser cleaning of acrylic urethane paint on the surface of 7050 aluminum alloy, and the effects of laser power, scanning speed and repetition frequency on the paint removal rate and surface roughness were investigated. Quantitative analysis of the paint removal rate was achieved by binarizing the super depth of field image of the substrate surface. The results show that as the laser power increases, the paint removal rate gradually increases and the surface roughness first decreases and then increases. As the scanning speed and repetition frequency increase, the paint removal rate increases and then decreases, and the surface roughness decreases and then increases. A generalized regression neural network (GRNN) model was used to establish the correlation density function between laser process parameters and cleaning quality. The best combination of parameters for the laser paint removal process was obtained by multi-objective optimization of the model through the multi-objective sparrow search algorithm (MOSSA). With this laser process parameter, the paint removal rate was 99.16% and the surface roughness was 1.32 μm.