The　focus　of　this　article　is　on　the　effectiveness　of　using　Convolutional　Neural　Networks　(CNN)　in　intelligent　process　design　systems.　Traditional　process　design　methods　have　some　limitations　in　data　processing,　generalization　ability,　computational　efficiency,　and　interpretability.　Therefore,　this　article　attempts　to　introduce　a　CNN　algorithm　model　to　improve　the　process　of　pattern　recognition　and　feature　extraction.　This　article　also　introduces　the　background　and　current　research　results　of　intelligent　process　design,　and　in　the　experimental　section,　demonstrates　the　significant　improvements　that　CNN　can　bring　in　process　design.　Through　three　specific　experiments,　it　has　been　confirmed　that　CNN　has　significant　advantages　in　processing　speed　and　accuracy　compared　to　traditional　methods.　It　evaluates　the　performance　of　CNN　in　intelligent　process　design　systems　based　on　three　experiments.　The　accuracy　evaluation　experiment　shows　that　as　the　training　period　increases,　the　accuracy　of　the　model　improves　from　the　initial　50%　to　100%,　which　demonstrates　the　effectiveness　of　the　CNN　model.　In　the　efficiency　comparison　experiment,　the　CNN　algorithm　model　only　takes　2.5　seconds　to　process　the　same　task,　while　the　traditional　method　takes　5　seconds.　This　data　conclusion　demonstrates　the　high　efficiency　of　the　CNN　model.　In　the　final　generalization　ability　experiment,　the　accuracy　of　the　CNN　model　reached　80%　on　dataset　A,　while　it　slightly　decreased　to　65%　on　dataset　B,　indicating　that　the　CNN　model　has　good　adaptability,　but　there　is　still　room　for　improvement.　The　above　experimental　results　demonstrate　the　high　accuracy,　excellent　processing　efficiency,　and　good　generalization　ability　of　CNN　models　in　intelligent　process　design,　providing　strong　evidence　for　their　potential　in　practical　process　design　applications.　Future　work　can　focus　on　further　enhancing　the　generalization　ability　and　interpretability　of　models　to　better　adapt　to　complex　process　design　requirements.　©　2024　IEEE.