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As one of the key technologies of mobile edge computing, computing offloading can greatly reduce the user's waiting time by moving the task to the edge server.For the problem of computing offloading for tasks with dependencies, in order to solve the problem of losing the structural information in the previous literature when the directed acyclic graph representing the task dependency is input into the neural network, came up with a directed acyclic graph neural network(DAGNN), and combine it with deep reinforcement learning to make decisions about offloading scheduling. The process of offloading decision is described as Markov decision process, and the Q value of each offloading action in the deep reinforcement learning algorithm is evaluated by the DAGNN proposed in this paper, and then the offloading scheduling decision is made. Simulation results show that the proposed algorithm performs better than all other baseline algorithms under various conditions, and shows good stability and versatility.