Accurately　estimating　the　number　of　individuals　contained　in　an　image　is　the　purpose　of　the　crowd　counting.　It　has　always　faced　two　major　difficulties:　uneven　distribution　of　crowd　density　and　large　span　of　head　size.　Focusing　on　the　former,　most　CNN-based　methods　divide　the　image　into　multiple　patches　for　processing,　ignoring　the　connection　between　the　patches.　For　the　latter,　the　multi-scale　feature　fusion　method　using　feature　pyramid　ignores　the　matching　relationship　between　the　head　size　and　the　hierarchical　features.　In　response　to　the　above　issues,　we　propose　a　crowd　counting　network　named　CCST　based　on　swin　transformer,　and　tailor　a　feature　adaptive　fusion　regression　head　called　FAFHead.　Swin　transformer　can　fully　exchange　information　within　and　between　patches,　and　effectively　alleviate　the　problem　of　uneven　distribution　of　crowd　density.　FAFHead　can　adaptively　fuse　multi-level　features,　improve　the　matching　relationship　between　head　size　and　feature　pyramid　hierarchy,　and　relief　the　problem　of　large　span　of　head　size　available.　Experimental　results　on　common　datasets　show　that　CCST　has　better　counting　performance　than　all　weakly　supervised　counting　works　and　great　majority　of　popular　density　map-based　fully　supervised　works.