Crowd　counting　is　a　hot　issue　in　visual　data　processing.　It　also　plays　an　important　role　in　the　field　of　video　surveillance,　social　security,　and　traffic　control.　However,　most　of　the　existing　crowd　counting　methods　always　adopt　a　mount　of　training　data　or　point-level　annotation　to　learn　the　mapping　relationships　between　images　and　density　maps,　which　would　cost　much　human　labor.　In　this　paper,　we　propose　a　new　weakly　semi-supervised　crowd　counting　method　which　uses　less　count-level　data　for　data　training.　In　particular,　we　extend　the　classical　smoothness　assumption　and　design　a　many-to-many　Region　Feature　Smoothness　Assumption　to　deal　with　the　uneven　density　distribution　problem　within　crowd　region.　Further,　we　adopt　hypergraph　representation　to　explore　the　complex　high-order　relationship　for　different　crowd　regions.　Besides,　we　design　a　multi-scale　dynamic　hypergraph　convolutional　module　and　hyperedge　contrastive　loss.　Extensive　experiments　have　been　conducted　on　five　public　datasets.　The　experimental　results　show　that　the　proposed　method　outperforms　the　state-of-the-art　ones.