Efficient　human　resource　management　(HRM)　is　essential　for　company　achievement　in　today’s　fast-paced　corporate　world.　Businesses　must　find　effective　ways　to　retrieve　and　categorise　the　ever-growing　amount　of　HR-related　knowledge.　This　work　presents　a　new　method　for　retrieving　and　classifying　HRM　data　using　machine　learning.　Modern　natural　language　processing　(NLP)　and　CNN　methods　are　used　by　the　algorithm　we　developed　to　handle　unorganized　human　resources　(HR)　information,　including　worker　records,　job　postings,　and　certificates.　HR　decision-making　is　facilitated　by　the　system’s　ability　to　derive　insightful　information　from　the　information　using　sophisticated　text　mining　and　machine　learning　algorithms.The　two　main　parts　of　the　method　are　classification　and　data　extraction.　HR　workers　can　more　easily　obtain　the　required　knowledge　thanks　to　information　retrieval,　which　makes　it　possible　to　search　HR　data　quickly　and　accurately.　Contrarily,　categorisation　optimises　the　division　of　human　resources　information　into　pertinent　classifications,　including　job　positions,　competencies,　and　achievement　grades.　We　assess　our　algorithm’s　effectiveness　on　various　datasets　from　actual　HR　datasets.　The　outcomes　show　how　well　our　strategy　works　to　streamline　HRM　procedures,　cut　down　on　manual　labour,　and　increase　the　precision　of　decisions.　Furthermore,　our　technology　is　compatible　with　corporate　human　resources　offices　and　educational　settings　because　it　complies　with　worldwide　university　requirements.This　study　belongs　to　the　growing　body　of　knowledge　in　HRM.　It　provides　a　useful　tool　for　businesses　looking　to　improve　employee　relations,　simplify　HR　procedures,　and　attract　and　retain　talent.　The　suggested　method　is　a　valuable　resource　for　academics　and　businesses　alike　because　of　its　versatility　and　compliance　with　global　educational　norms.　©　2024　SCPE.