Named　entity　recognition　is　a　practical　approach　to　automatically　identifying　named　entities　in　text　and　data.　Towards　the　vast　amount　of　data　generated　in　our　daily　life,　Artificial　Intelligence　(AI)　with　economical　but　powerful　computing　resources　are　inevitably　becoming　the　most　appropriate　method　for　name　entities　classification.　However,　the　results　of　currently　popular　methods　may　also　lack　the　aiming　super　high　accuracy　to　specific　data　and　the　interests　of　the　subscribers.　This　paper　proposes　a　named　entity　recognition　model　based　on　entity　trigger　reinforcement　learning　for　automatic　Chinese　recognition.　Unlike　existing　named　entity　recognition　methods,　the　proposed　method　can　support　multiple　inputs.　The　accuracy　proof　and　performance　evaluation　show　that　the　proposed　method　is　provable　robotic　in　entity　categories　classification　and　efficient　in　practice.　©　2022　IEEE.