Effective　tourist　demand　forecasting　is　crucial　for　company　operations　and　destination　management.　Furthermore,　tourists　may　plan　better　personalized　multi-attraction　itineraries　based　on　demand　forecasting　to　avoid　travel　peaks　and　improve　the　enjoyment　of　their　vacation.　This　study　developed　a　unique　deep　learning　model　called　the　convolution　block　attention　module　(CBAM)　that　is　built　on　convolutional　blocks　and　attention　modules　to　estimate　tourism　demand　precisely.　Then,　the　passenger　flow　grid　map　was　extracted　from　mobile　phone　signaling　data.　To　forecast　the　subsequent　period　of　the　passenger　flow　grid　map,　the　CBAM　model　uses　the　multi-channel　spatial-temporal　grid　graph　that　is　built　by　multiple　successive　passenger　flow　grid　maps.　Finally,　the　forecasted　passenger　flow　grid　map　was　used　to　derive　the　tourist　demand　for　multi-attractions　for　the　next　period.　The　analysis　of　mobile　phone　signaling　data　from　Beijing　and　Xiamen　using　the　proposed　model　reveals　that　its　mean　absolute　percentage　error　(MAPE)　is　8.11%,　which　is　lower　than　other　benchmark　deep　learning　models.