Abstract: To cope with the shortages of low sampling efficiency and difficult convergence of traditional Bayesian method under high-dimension parameters, a Bayesian based finite element model is proposed by the base of a multi-chain differential evolution algorithm. Based on the standard Markov chain Monte Carlo (MCMC) method, a differential evolution algorithm is introduced, and a random difference operation among multiple Markov chains is derived from the size and direction of an adaptive selection condition distribution to quickly approximate the target distribution. A subspace sampling algorithm which uses adaptive selection to select good parameter dimensions for sampling is introduced to improve sampling efficiency. Also, an anomaly Markov chain detection algorithm which detects and eliminates abnormities in Markov chains in the non-stationary period is introduced to improve the sampling efficiency in the stationary phase. The correction results of a simply-supported-beam model and a four-floor-frame-structure model show that the proposed method has a higher correction accuracy, better noise resistance, and better correction effects than that of DRAM algorithm under high order frequency and mode shapes, which provides a new way to improve the computational accuracy in uncertainty model correction.