User　trajectories　are　denser　and　highly　dynamic　in　mobile　crowdsensing　(MCS)　system,　rendering　traditional　privacy　budget　allocation　schemes　insufficient.　Additionally,　the　protection　of　semantic　location　privacy　is　often　neglected　in　these　schemes,　making　them　vulnerable　to　inference　attacks.　To　address　these　deficiencies,　a　user　trajectory　privacy　protection　strategy　based　on　deep　reinforcement　learning　is　proposed　in　this　article.　First,　a　differential　privacy-based　user　trajectory　privacy　protection　algorithm　(DP-upps)　is　designed　to　protect　the　privacy　by　perturbing　the　extracted　trajectory　feature　points.　Then,　a　deep　reinforcement　learning-based　privacy　budget　allocation　algorithm　(DRL-pbas)　is　introduced.　The　privacy　budget　is　dynamically　adjusted　by　deep　reinforcement　learning　option　to　continuously　adapt　to　environmental　changes　and　maximize　benefits.　After　that,　a　DRL-pbas　based　user　privacy　protection　strategy　(DRL-UPPS)　is　proposed,　integrating　semantic　location　privacy　protection.　This　approach　combines　the　previous　two　algorithms,　allowing　the　privacy　budget　to　be　allocated　in　a　way　that　effectively　balances　the　protection　of　physical　and　semantic　location　privacy　and　data　quality.　Ultimately,　a　large　number　of　simulation　experiments　are　conducted　based　on　real　datasets.　The　experiments　demonstrate　that　DRL-UPPS　can　effectively　balance　privacy　protection　and　data　quality,　resisting　the　privacy　attacks.　Compared　with　other　strategies,　DRL-UPPS　improves　comprehensive　privacy　protection　capability　by　approximately　10%　and　data　utility　by　approximately　8%.