Weakly　supervised　crowd　counting　involves　the　regression　of　the　number　of　individuals　present　in　an　image,　using　only　the　total　number　as　the　label.　However,　this　task　is　plagued　by　two　primary　challenges:　the　large　variation　of　head　size　and　uneven　distribution　of　crowd　density.　To　address　these　issues,　we　propose　a　novel　Hypergraph　Association　Crowd　Counting　(HACC)　framework.　Our　approach　consists　of　a　new　multi-scale　dilated　pyramid　module　that　can　efficiently　handle　the　large　variation　of　head　size.　Further,　we　propose　a　novel　hypergraph　association　module　to　solve　the　problem　of　uneven　distribution　of　crowd　density　by　encoding　higher-order　associations　among　features,　which　opens　a　new　direction　to　solve　this　problem.　Experimental　results　on　multiple　datasets　demonstrate　that　our　HACC　model　achieves　new　state-of-the-art　results.