In edge computing switching strategy, aiming at the problems of high transmission delay and poor environmental adaptability of Markov decision process(MDP), this paper proposed an edge computing switching strategy integrating fuzzy logic and Markov decision process. The method used fuzzy logic algorithm to fuzzify the system parameters, and introduced the fuzzy value into the fitness function to ensure the effective fusion of system parameters. The differential evolution algorithm solved the maximum problem of fitness function, which could select the optimal rules of the environment and improve the adaptability of edge computing to the environment. The algorithm introduced the fitness function into MDP, which improved the comprehensive performance of the system. The scheme took the mobile intelligent device as the initiator of task unloading and the edge server as the object of task unloading. This paper simulated the one-dimensional MDP switching strategy, one-dimensional delay only MDP switching strategy, two-dimensional MDP switching strategy, fuzzy logic MDP switching strategy, minimum distance switching algorithm and minimum delay switching algorithm. The simulation results show that the average task execution time of the edge computing switching strategy of fuzzy logic MDP is 608.8 s, which are 27.2%, 8.6%, 37.1%, 41% and 22.3% lower than one-dimensional MDP switching strategy, one-dimensional delay only MDP switching strategy, two-dimensional MDP switching strategy, minimum distance switching algorithm and minimum delay switching algorithm respectively. This scheme not only improves the environmental adaptability of the edge computing switching strategy based on MDP, but also greatly reduces the transmission delay of edge computing.