Particle swarm optimization (PSO) is a global search algorithm based on population, which is characterized by simple principle and stable and efficient search. It is widely used in the field of route planning, but it is defective when falling into local optimum and in convergence speed. In this paper, random walk strategy is introduced to the mission weight and survival weight of UAV. By changing the inertia weight of particles according to certain rules, PSO’s defects can be effectively avoided, and UAV’s efficiency in finding the optimal path can be improved. On the other hand, in order to provide a criterion or reference to evaluate planned path, it is necessary to construct a survival state probability model to evaluate UAV flight path points. The route planning model of the random walk particle swarm optimization algorithm (RWPSO) was combined with the Markov chain survival state randomness model, thereby building up a route planning model for estimating the survival probability of path points. Simulation results show that RWPSO based on random walk of task weight, survival weight, and task survival weight was more efficient than PSO and quantum particle swarm optimization (QPSO) in optimization. A model describing the change of survival probability of UAV was thus obtained successfully by combining Markov chain with RWPSO. The framework can be extended to route and mission planning in complex scenes with radiation sources, weapons, or electromagnetic interference.