Abstract: As edge computing and cloud computing develop in a rapid speed and integrate with each other, resources and services gradually offload from the core network to the edge of the network. Efficient computation offloading algorithm is one of the most important problems in mobile edge computing networks. In order to improve the performance of the algorithm, a computation offloading algorithm with path selection based on K-shell influence maximization is proposed. The K-shell method is used to grade the edge servers by applying the convertibility and maximizing influence model of similar problems. Otherwise, considering the problem of excessive load of edge servers, path overlap (PO) algorithm is constructed, and indicators such as the communication quality, interaction strength, and queue processing ability, etc. are introduced to optimize the performance of the algorithm. The offloading path problem of the optimization calculation task is transformed into the social network impact maximization problem. Based on the idea of maximizing K-shell influence, greedy and heuristic algorithms are optimized and improved, and the K-shell influence maximization computation offloading (Ks-IMCO) algorithm is proposed to solve the problem of computational offloading. Through the comparative analysis of Ks-IMCO and random allocation (RA), path selection with handovers (PSwH) algorithm experiments, the energy consumption and delay of Ks-IMCO algorithm have been significantly improved, which can effectively improve the efficiency of edge computing network computing offloading.