With　the　emergence　of　various　intelligent　applications,machine　learning　technologies　face　lots　of　challenges　including　large-scale　models,application　oriented　real-time　dataset　and　limited　capa-bilities　of　nodes　in　practice.Therefore,distributed　machine　learning(DML)and　semi-supervised　learning　methods　which　help　solve　these　problems　have　been　addressed　in　both　academia　and　indus-try.In　this　paper,the　semi-supervised　learning　method　and　the　data　parallelism　DML　framework　are　combined.The　pseudo-label　based　local　loss　function　for　each　distributed　node　is　studied,and　the　stochastic　gradient　descent(SGD)based　distributed　parameter　update　principle　is　derived.A　demo　that　implements　the　pseudo-label　based　semi-supervised　learning　in　the　DML　framework　is　conduc-ted,and　the　CIFAR-10　dataset　for　target　classification　is　used　to　evaluate　the　performance.Experi-mental　results　confirm　the　convergence　and　the　accuracy　of　the　model　using　the　pseudo-label　based　semi-supervised　learning　in　the　DML　framework.Given　the　proportion　of　the　pseudo-label　dataset　is　20％,the　accuracy　of　the　model　is　over　90％when　the　value　of　local　parameter　update　steps　be-tween　two　global　aggregations　is　less　than　5.Besides,fixing　the　global　aggregations　interval　to　3,the　model　converges　with　acceptable　performance　degradation　when　the　proportion　of　the　pseudo-label　dataset　varies　from　20％to　80％.