Resistance　spot　welding　is　a　complex　process　in　which　many　factors　interact.　Given　the　small　size　of　data　sets　available　and　the　complex　nature　of　unstable　processes,　it　is　difficult　to　establish　an　accurate　mathematical　model　to　predict　the　parameters　of　resistance　spot　welding.　An　optimal　computing　method　for　solving　this　problem　is　presented.　The　method　combines　Bayes-XGBoost　with　the　Particle　Swarm　Optimization　(PSO)　algorithm　to　select　suitable　features　and　to　enable　the　optimal　combinations　of　samples　for　0.15　mm　nickel　sheets　and　for　0.4　mm　stainless　steel　battery　positive　caps;　The　non-linear　slicing　ability　and　anti-overfitting　mechanism　of　eXtreme　Gradient　Boosting　(XGBoost)　are　used　to　train　forward　spot　welding　parameters;　and　Bayesian　optimization　is　applied　to　the　XGBoost＇s　optimal　parameter　selection.　The　method　uses　the　global　optimization　feature　of　Particle　Swarm　Optimization　(PSO)　to　predict　the　backward　process　parameters　with　variable　target　values　such　that　the　optimal　process　parameters　are　obtained.　Compared　with　other　algorithms　mentioned　in　this　paper,　this　method　offers　more　comprehensive　performance　and　possesses　better　capabilities　to　effectively　assist　in　the　spot　welding　process,　which　are　demonstrated　by　the　resistance　spot　welding　experiments　performed.　©　2021,　Science　Press.　All　right　reserved.