GPS positioning data processing algorithm based on the Markov Chain and Monte Carlo(MCMC) particle filter was proposed to solve the problem that GPS positioning data processing based on the simple particle filter suffers from severe sample degeneracy. The standard MCMC method, Metropolis Hsstings (MH) sampling, was incorporated into the filtering framework , and was applied to the GPS positioning data processing problem. It is combined the particle filter with the GPS system nonlinear dynamic state-space model. The MCMC method is adapted to solve the degeneracy phenomenon of particle filter (PF). It is an effective algorithm to the nonlinear and non-Gaussian state estimation problem of GPS positioning data processing. Experimental results based on the real GPS data showed that the MCMC particle filter can increase the sample variety and reduce sample degeneracy. GPS positioning data processing based on the MCMC particle filter is much more accurate, compared with GPS positioning data processing based on the simple particle filter. Moreover, the MCMC particle filter can provide a high accurate positioning value as an aided plan when the GPS signal quality is poor.