To　solve　the　problems　of　the　current　graph　pooling　method　based　on　the　node　sampling,　such　as　the　simplistic　strategy　of　node　importance　evaluation　and　the　massive　loss　of　sub-structure　feature　information　of　graph,　a　sub-structure　representative　hierarchical　pooling　model　based　on　node　sampling　dubbed　as　SsrPool　was　proposed.　This　method　mainly　includes　a　sub-structure　representative　node　selection　module　and　a　sub-structure　representative　node　feature　generation　module.　First,　considering　both　the　structure　and　feature　information　of　the　graph,　different　methods　were　used　to　evaluate　the　node　importance,　and　the　collaboration　of　varying　importance　scores　generated　a　robust　node　ranking　to　guide　node　selection　in　the　sub-structure　representative　node　selection　module.　Second,　sub-structure　features　information　was　retained　through　feature　fusion　in　the　sub-structure　representative　features　generation　module.　By　combining　the　SsrPool　model　with　existing　graph　neural　networks,　experimental　results　of　graph　classification　on　different　public　datasets　demonstrate　the　effectiveness　of　SsrPool.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.