Abstract: In edge computing enhanced LEO satellite networks，collaboratively processing ground tasks by satellite clusters could effectively shorten the user response delay.The optimization of joint offloading decision and resource allocation of satellite clusters was studied，and was described as a mixed integer programming problem.A satellite edge computing enhanced deep learning-based offloading algorithm（DLOA）was adopted.The algorithm used multiple parallel DNN networks to generate offloading decisions and adopted experience replay to store the newly generated decisions.Particularly，the convergence rate of heterogeneous DNN with different hidden layer structure is 18% higher than that of homogeneous DNN with same hidden layer structure，and the ratio of convergent value to optimal value is basically 1，which can be considered to have converged to the optimal.In addition，the influence of the number of DNN on the proposed algorithm was discussed，and the simulation results show that the near-optimal effect can be obtained by using a small number of DNN.By studying the task completion rate of different algorithms under different task scales，the results show that DLOA algorithm can significantly improve the completion rate by using heterogeneous DNNs and optimized resource allocation schemes，which is twice as high as the single satellite scheme，and 20% higher than the binary particle swarm algorithm.

Key words: edge computing, computation offloading, LEO satellite network, deep learning, convergence performance