Sparse　Mobile　Crowdsensing　(Sparse　MCS)　selects　a　small　part　of　sub-areas　for　data　collection　and　infers　the　data　of　other　sub-areas　from　the　collected　data.　Compared　with　Mobile　Crowdsensing　(MCS)　that　does　not　use　data　inference　methods,　Sparse　MCS　saves　sensing　costs　while　ensuring　the　quality　of　global　data.　However,　the　existing　research　works　on　Sparse　MCS　only　focus　on　selecting　a　small　part　of　sub-areas　with　higher　value.　It　does　not　consider　whether　the　recruited　participants　can　collect　the　data　of　the　required　sub-areas,　and　also　ignores　the　value　of　other　data　collected　by　the　participants.　In　order　to　solve　the　limitations　of　traditional　methods　in　sub-areas　selection,　this　paper　starts　from　the　perspective　of　participants　and　concentrates　on　the　contribution　of　the　data　collected　by　each　participant　to　the　entire　collection　task.　All　the　data　contributions　collected　by　each　participant　will　become　the　basis　for　decision-making　for　the　participant＇s　choice.　And　correspondingly,　a　new　idea　to　deal　with　the　problem　of　participant　selection　under　Sparse　MCS　is　proposed.　In　view　of　the　fact　that　each　person＇s　daily　movement　trajectory　is　basically　stable,　and　the　data　collected　by　different　people　on　their　respective　trajectories　have　different　values,　this　paper　uses　this　regularity　and　difference　to　study　how　to　directly　recruit　participants　who　can　collect　high-value　data.　Furthermore,　the　participant　selection　problem　considered　in　this　paper　is　not　limited　to　the　data　collection　in　the　next　cycle,　but　directly　recruits　some　participants　to　continue　the　data　collection　task　in　the　next　multiple　cycles.　The　participant　selection　problem　that　spans　multiple　cycles　can　be　modeled　as　a　dynamic　decision-making　problem.　Since　heuristic　strategies　may　fall　into　a　local　optimal　solution,　this　paper　uses　reinforcement　learning　to　solve　the　participant　selection　problem:　We　use　the　participant　selection　system　as　an　agent　of　reinforcement　learning,　and　design　the　state,　action　and　reward　of　the　reinforcement　learning　model　in　detail.　Factors　such　as　historical　selection　participant　status,　sub-areas　data　collection　status　and　date　are　considered　in　the　state.　The　user　number　is　regarded　as　an　action　in　reinforcement　learning,　and　the　reward　is　reflected　by　the　final　data　inference　error.　In　order　to　avoid　the　problem　of　excessive　number　of　actions,　this　paper　sets　the　action　to　select　only　one　participant　at　a　time　until　the　maximum　number　of　participants　is　reached,　instead　of　selecting　a　group　of　participants　at　a　time.　This　paper　will　discuss　in　detail　the　difference　between　the　two　action　modes.　To　deal　with　the　explosion　of　the　number　of　state　spaces,　we　use　deep　reinforcement　learning　algorithm　Deep　Q　Network　(DQN)　to　train　the　Q-function,　aiming　to　give　the　best　choice　for　judging　which　participants　to　recruit　in　a　specific　state.　This　framework　was　verified　on　a　real　data　set　of　air　quality　in　Beijing　for　two　months　and　the　movement　trajectories　of　more　than　one　hundred　users.　Compared　with　several　baseline　policies,　our　proposed　participant　recruitment　strategy　can　achieve　higher　data　estimation　accuracy　under　a　limited　number　of　users.　©　2022,　Science　Press.　All　right　reserved.