The energy Internet (EI) aims to build a sustainable energy ecosystem by connecting diverse energy sources and prosumers. Mobile crowd sensing (MCS) enables efficient data collection for monitoring and aggregation from distributed devices. Given the complex behavior of workers driven by self-interest, recruiting trustworthy, high-quality, and inexpensive workers remains a significant challenge in research and practice. Previous studies often assume that worker characteristics are known or can be obtained after data collection. However, evaluating worker qualities is quite challenging in the face of multisource data and complex workers. To address this, we propose a completion rate learning-based multiarmed bandit reverse auction (CRL-MABA) scheme for identifying and selecting high-quality workers in MCS. Our CRL-MABA scheme first proposes a spatial–temporal upper confidence bound (STUCB) method to recruit workers, considering both the quality of workers for exploitation and the spatiotemporal features for exploration. In addition, the dual-stage data estimation mechanism (DSDEM) and long-term and short-term memory learning (LTSTML) are designed to identify workers accurately and efficiently. Importantly, our proposed scheme avoids the impractical assumptions in previous works while satisfying important criteria, such as truthfulness, individual rationality, and computational efficiency. The effectiveness of our scheme is demonstrated through extensive experimental results, which show its superiority over existing strategies.