Many　science,　technology　and　innovation　(STI)　resources　are　attached　with　several　different　labels,　such　as　IPC　and　CPC　for　patents,　and　PACS　(Physics　and　Astronomy　Classification　Scheme)　numbers　for　scientific　publications.　This　problem　is　well　known　as　the　multi-label　classification.　Though　there　are　a　number　of　approaches　and　open-source　tools　for　this　task　in　the　literature　that　work　well　on　benchmark　datasets,　real-world　is　more　complex　in　terms　of　both　the　number　and　hierarchy　of　labels.　This　work　aims　to　compare　comprehensively　the　performance　of　three　state-of-the-art　tools,　Dependency　LDA,　Scikit-Multilearn　and　Neural　Classifier　on　Scigraph　of　academic　resource　data.　It　is　found　that　Neural　Classifier　works　better　on　an　unbalanced　distribution　dataset　with　more　complex　hierarchical　structure　and　a　larger　number　of　label　scale　in　terms　of　Micro　F1,　Micro　F1　and　Hamming　Loss　than　the　other　two　tools.　On　the　basis　of　our　comparisons,　several　directions　are　suggested　in　the　near　future.　©　2020　ACM.