[Objective]　This　study　constructs　a　hierarchical　multi-label　classification　model　for　children’s　literature,　aiming　to　realize　the　automatic　classification　of　children’s　books,　guiding　young　readers　to　select　books　suitable　for　their　development　needs.　[Methods]　We　materialized　the　concept　of　graded　reading　into　a　hierarchical　classification　label　system　for　children’s　literature.　Then,　we　built　ERNIE-HAM　model　using　deep　learning　techniques　and　applied　it　to　the　hierarchical　multi-label　text　classification　system.　[Results]　Compared　with　the　four　pre-training　models,　the　ERNIE-HAM　model　performed　well　in　the　second　and　third　hierarchical　classification　levels　for　children’s　books.　Compared　to　the　single-level　algorithm,　the　hierarchical　algorithm　improved　the　AU　(-PRC　---)　values　for　the　second　and　third　levels　by　about　11%.　Compared　to　the　two　hierarchical　multi-label　classification　models,　HFT-CNN　and　HMCN,　the　ERNIE-HAM　model　improved　the　third　level　by　12.79%　and　6.48%　in　the　classification　results,　respectively.　[Limitations]　The　overall　classification　performance　of　the　proposed　model　can　be　further　improved,　and　future　work　should　focus　on　expanding　the　dataset　and　refining　the　algorithm　design.　[Conclusions]　The　ERNIE-HAM　model　is　effective　in　the　hierarchical　multi-label　classification　for　children’s　literature.　©　2021　Seorim.　All　rights　reserved.