A hybrid particle swarm optimization (HPSO) approach is proposed to solve the optimization problem of the maximum entropy model oriented to Bayesian prior distribution. HPSO introduces chaos mechanism to create better initial species population, and the power-function carrier is adopted to improve the ergodicity and the sufficiency of the chaos mechanism. Then HPSO uses an inertia weight, which can balance global and local searching capability and fasten convergence speed. A nonlinear constrained optimization model of prior distribution based on the principle of maximum entropy is set up. By using Lagrange multiplier this constrained optimization problem is transformed to a non-constrained optimal one, which is solved by PSO and HPSO algorithm. The simulation example shows that HPSO not only has a better performance at the aspect of solution precision but also converges more quickly.