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Aiming at the future demand for joint scheduling and optimization of computing and networking re-sources in low Earth orbit (LEO) satellite systems, a deep reinforcement learning-based LEO computing power routing scheme is proposed to address the low efficiency and utilization of multi-dimensional re-source collaboration in LEO satellite networks. Based on the computing power routing protocol of a computing and networking orchestration controller, an optimal delay model for computing power sched-uling is established. Additionally, an intelligent algorithm for LEO computing power routing based on Deep Q-Network is developed and implemented. This algorithm models the LEO satellite computing power routing addressing as a Markov decision process, defining a state space that includes features such as business, topology, and computing power, as well as a reward function related to optimal delay. After model training and simulation analysis, the converged intelligent algorithm significantly improves the comprehensive utilization efficiency of computing and networking resources compared to benchmark al-gorithms, reduces the time required for task processing, and optimizes user experience.