In　order　to　assist　judicial　staff　in　analyzing　the　sentencing　of　the　case,　this　article　uses　the　named　entity　identification　approach　to　extract　the　sentencing　elements　from　the　theft　records.　This　study　adopts　the　BERT　named　entity　recognition　approach,　primarily　employing　the　pre-trained　model　of　BERT-WWM-EXT　and　CHINESE,　and　extracts　the　named　entity　recognition　dataset　based　on　stolen　papers　in　Fuzhou　City　in　recent　years.　The　F1　value　of　the　experimental　model　findings　was　70.05%,　which　essentially　made　it　possible　to　extract　the　punishment　components　from　the　theft　records.　However,　the　F1　value　of　experimental　data　is　also　lower　than　the　reference　evaluation　index　of　Chinese　Machine　Reading　Comprehension　Data　(CJRC)　for　the　judicial　area　published　online　due　to　manual　labeling　errors,　data　processing　errors,　a　small　number　of　datasets,　and　machine　restrictions.　©　2023　Copyright　held　by　the　owner/author(s).