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　capabilities　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　industry.　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　conducted,　and　the　CIFAR-10　dataset　for　target　classification　is　used　to　evaluate　the　performance.　Experimental　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　between　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%.　Copyright　©　by　HIGH　TECHNOLOGY　LETTERS　PRESS.