Incentive mechanisms play a crucial role in mobile crowdsensing between mobile users, third-party platforms, and clients. However, existing mechanisms often do not sufficiently consider users’ future potential and past contributions to data sharing when recruiting or retaining them. Moreover, few people consider that the client in this mechanism requires high-quality data collection for training artificial intelligence models. To solve these problems, this paper proposes an innovative mobile crowdsensing incentive mechanism based on reverse auctions. This mechanism not only attracts users who provide high-quality data but also helps the client collect an appropriate amount of cost-effective data for model training. This is achieved by setting prerequisites for the release of tasks by the platform, that is, only after the client’s model training effect reaches a certain level of improvement, the platform can release new tasks for data collection. Additionally, when recruiting users to participate in auctions, the platform will selectively recruit based on their potential value to ensure high-quality data collection. In cases where users might consider leaving the platform, measures for retention will be implemented. This will involve offering rewards adjusted proportionally according to their potential and contribution value to encourage them to stay. The comprehensive simulation results demonstrate the effectiveness of this new mechanism, highlighting its superior performance over existing mechanisms.