Concerning the high computational complexity and communication overhead of wireless network node localization algorithm based on message passing algorithm, a ranging-based hybrid message passing node localization method with low complexity and cooperative overhead was proposed. The uncertainty of the reference nodes was taken into account to avoid error accumulation, and the messages on factor graph were restricted to be Gaussian distribution to reduce the communication overhead. Firstly, the factor graph was designed based on the system model and the Bayesian factorization. Secondly, belief propagation and mean filed methods were employed according to the linear state transition model and the nonlinear ranging model to calculate the prediction messages and the cooperation messages, respectively. Finally, in each iteration, the non-Gaussian beliefs were approximated into Gaussian distribution by Taylor expansions of the nonlinear terms. The simulation results show that the positioning accuracy of the proposed algorithm is compareable to that of Sum-Product Algorithm over a Wireless Network (SPAWN), but the information transmitted between nodes decreases from a large number of particles to mean vector and covariance matrix, and the comupational complexity is also dramatically reduced.