Simulated annealing algorithm is an effective method for solving unconstrained optimization problems, but it has the disadvantages of poor accuracy, easy to fall into local optimum and slow convergence when solving travel quotient problems. In order to improve the above problems, a parallel simulated annealing algorithm based on Spark platform is proposed in this paper. The cooling function of the simulated annealing algorithm is modified to construct the solution space of the travel quotient problem, the local search capability is enhanced by using the large neighborhood search technique and the 2-opt operator, the global search capability is enhanced by introducing the OX crossover idea, and the crossover cooperative experiment parallel strategy is proposed to be implemented in parallel with the Spark platform. A number of TSPLIB datasets are selected for simulation experiments to test both solution quality and running time, and to compare the experiments with other parallel algorithms of Spark framework.The simulation results show that the solution accuracy of this method is greatly improved, and the solution speed is 3-10 times higher than other algorithms, which can effectively solve the traveling salesman problem.