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The optimal unloading strategy directly affects the time delay and energy consumption of computing task unloading, so a mobile edge computing task unloading method based on reinforcement learning method is proposed. Firstly, the form of computing task offloading of mobile devices is analyzed in detail, and the energy consumption, transmission power, transmission rate and other related parameter values of computing task offloading are obtained based on the analysis results, so as to establish a mobile edge computing task offloading model. At last, based on the established unloading model and Q-Learning algorithm, we implement reinforcement learning on computing tasks to find the best unloading strategy of computing tasks, so as to realize real-time unloading of mobile edge computing tasks. The experimental results show that when using this method to unload mobile edge computing tasks, the unloading energy consumption is low and the delay is small.