In　recent　years,　a　large　number　of　Chinese　electronic　texts　have　been　produced　in　the　process　of　information　construction　in　various　fields.　Identifying　specific　entities　in　these　electronic　texts　has　become　a　major　research　focus.　Most　existing　research　methods　use　radicals　to　extract　the　glyph　features　of　Chinese　characters　but　have　seen　its　limitation.　This　paper　extracts　the　features　of　Chinese　characters　from　three　aspects:　glyph　features,　phonetic　features,　and　character　features,　and　improves　conventional　feature　extraction　methods　for　each　kind　of　feature.　A　new　named　entity　recognition　method　(AIP)　is　proposed　by　transforming　Chinese　characters　into　corresponding　images　for　glyph　feature　extraction,　dividing　pinyin　into　initials,　vowels,　and　tones　for　phonetic　feature　extraction,　and　fine-tuning　the　A　Lite　Bert　model　for　character　feature　extraction　to　improve　the　performance　of　the　model.　This　paper　compares　the　performance　of　the　AIP　model　and　mainstream　neural　network　models　on　Chinese　named　entity　recognition　tasks　on　commonly　used　data　sets　and　the　data　sets　in　specific　domains.　The　results　showed　that　AIP　achieved　better　results　than　the　related　work.　The　F1　values　on　the　two　data　sets　are　94.4%　and　80.5%,　respectively,　which　validates　the　model＇s　versatility.