During　human-robot　collaborations　(HRC),　robot　systems　must　accurately　perceive　the　actions　and　intentions　of　humans.　The　present　study　proposes　the　classification　of　standing　postures　from　standing-pressure　images,　by　which　a　robot　system　can　predict　the　intended　actions　of　human　workers　in　an　HRC　environment.　To　this　end,　it　explores　deep　learning　based　on　standing-posture　recognition　and　a　multi-recognition　algorithm　fusion　method　for　HRC.　To　acquire　the　pressure-distribution　data,　ten　experimental　participants　stood　on　a　pressure-sensing　floor　embedded　with　thin-film　pressure　sensors.　The　pressure　data　of　nine　standing　postures　were　obtained　from　each　participant.　The　human　standing　postures　were　discriminated　by　seven　classification　algorithms.　The　results　of　the　best　three　algorithms　were　fused　using　the　Dempster-Shafer　evidence　theory　to　improve　the　accuracy　and　robustness.　In　a　cross-validation　test,　the　best　method　achieved　an　average　accuracy　of　99.96%.　The　convolutional　neural　network　classifier　and　data-fusion　algorithm　can　feasibly　classify　the　standing　postures　of　human　workers.