The　task　of　crowd　counting　in　computer　vision　is　quite　difficult,　and　a　lot　of　excellent　work　has　emerged　recently.　Recent　research　has　suggested　network　algorithms　that　utilize　both　shallow　feature　information　and　deep　information　that　the　network　has　gathered.　However,　how　to　efficiently　use　these　different　scales　of　information　is　a　question　worth　considering.　We　propose　a　multi-scale　attention　network　(MSANet),　which　consists　of　a　convolution　module　and　a　spatial　attention　module.　The　feature　information　of　different　scales　extracted　from　the　middle　and　deep　layers　is　fused　to　obtain　richer　semantic　features　for　crowd　counting　task　detection.　At　the　same　time,　considering　that　the　fusion　of　different　scale　information　will　introduce　some　additional　background　information,　we　filter　the　redundant　background　information　by　introducing　the　attention　module.　Experiments　show　that　this　method　has　achieved　good　performance　improvement　on　public　datasets　JHU　+　+　and　UCF-QNRF.　©　2023　IEEE.