Fast　and　accurate　P-wave　arrival　picking　significantly　affects　the　performance　of　earthquake　early　warning　(EEW)　systems.　Automated　P-wave　picking　algorithms　used　in　EEW　have　encountered　problems　of　falsely　picking　up　noise,　missing　P-waves　and　inaccurate　P-wave　arrival　estimation.　To　address　these　issues,　an　automatic　algorithm　based　on　the　convolution　neural　network　(DPick)　was　developed,　and　trained　with　a　moderate　number　of　data　sets　of　17,717　accelerograms.　Compared　to　the　widely　used　approach　of　the　short-term　average/long-term　average　of　signal　characteristic　function　(STA/LTA),　DPick　is　1.6　times　less　likely　to　detect　noise　as　a　P-wave,　and　76　times　less　likely　to　miss　P-waves.　In　terms　of　estimating　P-wave　arrival　time,　when　the　detection　task　is　completed　within　1　s,　DPick＇s　detection　occurrence　is　7.4　times　that　of　STA/LTA　in　the　0.05　s　error　band,　and　1.6　times　when　the　error　band　is　0.10　s.　This　verified　that　the　proposed　method　has　the　potential　for　wide　applications　in　EEW.