Handwritten　signatures　are　widely　used　in　our　daily　lives,　which　focuses　on　how　to　obtain　handwritten　information　comprehensively　and　effectively.　Triboelectric　nanogenerators　can　sensitively　sense　externally　applied　trigger　signals　and　can　be　used　to　collect　user　handwriting　signals　and　related　features　for　handwriting　input　character　detection　and　user　identification.　In　this　paper,　an　intelligent　handwriting　input　panel　based　on　triboelectric　nanogenerator　is　proposed,　which,　combined　with　deep　learning　algorithms,　achieves　an　accuracy　of　99.32%,　98.96%,　99.14%,　and　99.53%　for　recognizing　handwritten　signals　in　Arabic　numerals,　English　words,　Chinese　characters,　and　different　users,　respectively.　It　is　also　possible　to　use　different　encoding　methods　for　encrypted　communication　to　transmit　signals　on　the　proposed　handwriting　input　panel,　demonstrating　its　potential　applications.　The　results　show　that　the　smart　handwritten　input　panel　has　great　potential　for　personal　handwritten　signature　recognition,　privacy　information,　and　human-computer　interaction.