Abstract: To achieve efficient and precise product shape optimization design, an ant colony algorithm-based product form optimization design model was constructed by establishing node selection probability formulas, dynamic contribution value update equations, and multi-objective fitness functions, with model computation implemented using the Matlab system. Taking an amphibious firefighting water supply and drainage robot as an engineering application case, the study was based on extensively collected samples including 15 representative image samples and 3 perceptual imagery vocabulary samples (rational, intelligent, agile). Guided by Kansei engineering theory, a perceptual imagery scale evaluation system was established, and morphological deconstruction was performed on the samples. After completing data reprocess, algorithm parameter configuration, and iterative computation in the Matlab system, the optimal form combination solution was output and presented in 3D. Finally, virtual reality (VR) simulation experiments were conducted to analyze and evaluate the output solutions, yielding 11 sets of valid experimental evaluation data. The results demonstrate that optimized solution 2 achieves significantly improved mean evaluation scores of 4.82 (rational), 4.91 (intelligent), and 4.91 (agile) across the three perceptual imagery dimensions compared to the baseline solution, indicating better alignment with users’ perceptual needs. This fully validates the model’s engineering practicality in achieving precise matching between product form and users perceptual requirements.