The uncertainty of tasks in mobile edge computing scenarios makes task offloading and resource allocation more complex and difficult. Therefore, a continuous offloading and resource allocation method of uncertain tasks in mobile edge computing is proposed. Firstly, a continuous offloading model of uncertain tasks in mobile edge computing is built, and the multi-batch processing technology based on duration slice partition is employed to address task uncertainty. A multi-device computing resource coordination mechanism is designed to improve the carrying capacity of computation-intensive tasks. Secondly, an adaptive strategy selection algorithm based on load balancing is put forward to avoid channel congestion and additional energy consumption caused by the over-allocation of computing resources. Finally, the uncertain task scenario model is simulated based on Poisson distribution, and experimental results show that the reduction of time slice length can reduce the total energy consumption of the system. In addition, the proposed algorithm can achieve task offloading and resource allocation more effectively and can reduce energy consumption by up to 11.8% compared with comparison algorithms.