In　this　paper,　a　new　user　identification　method　is　presented　using　real　environmental　human-computer-interaction　(HCI)　behavior　data　to　improve　method　usability.　User　behavior　data　in　this　paper　are　collected　continuously　without　setting　experimental　scenes　such　as　text　length,　action　number,　etc.　To　illustrate　the　characteristics　of　real　environmental　HCI　data,　probability　density　distribution　and　performance　of　keyboard　and　mouse　data　are　analyzed　through　the　random　sampling　method　and　Support　Vector　Machine(SVM)　algorithm.　Based　on　the　analysis　of　HCI　behavior　data　in　a　real　environment,　the　Multiple　Kernel　Learning　(MKL)　method　is　first　used　for　user　HCI　behavior　identification　due　to　the　heterogeneity　of　keyboard　and　mouse　data.　All　possible　kernel　methods　are　compared　to　determine　the　MKL　algorithm＇s　parameters　to　ensure　the　robustness　of　the　algorithm.　Data　analysis　results　show　that　keyboard　data　have　a　narrower　range　of　probability　density　distribution　than　mouse　data.　Keyboard　data　have　better　performance　with　a　1-min　time　window,　while　that　of　mouse　data　is　achieved　with　a　10-min　time　window.　Finally,　experiments　using　the　MKL　algorithm　with　three　global　polynomial　kernels　and　ten　local　Gaussian　kernels　achieve　a　user　identification　accuracy　of　83.03%　in　a　real　environmental　HCI　dataset,　which　demonstrates　that　the　proposed　method　achieves　an　encouraging　performance.